Somewhere there’s analysis small and large patches special

## [1] "!! low !! (this document showcases only the analysis done for the low disturbance)"

Design

Parameters

# R markdown

start_time = Sys.time()
knitr::opts_chunk$set(message = FALSE,
                      cache = FALSE,
                      autodep = FALSE)
recompute_lengthy_analyses = FALSE #Lengthy analyses have been suppressed. Set recompute_analyses = TRUE to redo them. 
# Disturbance & resource flows

disturbance_levels = c("low", "high")
n_disturbance_levels = length(disturbance_levels)

resource_flow_days = c(5, 9, 13, 17, 21, 25)
first_resource_flow = resource_flow_days[1]
# Sampling

total_frames = 125
volume_recorded_μl = 34.4
time_points = 0:7
time_points_without_t0 = 1:7
time_point_names = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")
sampling_days = c(0, 4, 8, 12, 16, 20, 24, 28)
first_time_point = 0
last_time_point = length(sampling_days) - 1
n_time_points = last_time_point + 1
nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2) #Videos taken for each time point for each culture. At t0 we took 12 videos of the large bottle from which we started the  cultures. Write why 2 at the end. 

videos_taken = data.frame(time_point = 0 : 7,
                          nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2))

n_videos_taken_t0 = nr_videos[1]

time_point_day = data.frame(
  time_point = first_time_point:last_time_point,
  day = sampling_days,
  video_replicates = nr_videos
)

videos_to_take_off = data.frame(culture_ID = NA,
                                time_point = NA,
                                file = NA) %>%
                     add_row(culture_ID = 137-110, time_point = 7, file = 137) %>%
                     slice(-1)

n_cultures = 110
total_number_of_video_rows = sum(nr_videos * n_cultures)
# Protists

protist_species = c("Ble", "Cep", "Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi", "Spi_te", "Tet")
protist_species_indiv_per_volume = paste0(protist_species, "_indiv_per_volume")
protist_species_indiv_per_ml = paste0(protist_species, "_indiv_per_ml")
protist_species_dominance = paste0(protist_species_indiv_per_ml, "_dominance")
protist_species_total = paste0(protist_species, "_tot_indiv")
n_protist_species = length(protist_species)
first_protist = protist_species[1]
last_protist = protist_species[n_protist_species]
species_IDD_with_13_threshold = c("Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi_te", "Tet")
species_IDD_with_13_threshold_indiv_per_volume = paste0(species_IDD_with_13_threshold, "_indiv_per_volume")
species_IDD_with_40_threshold = c("Ble", "Cep", "Spi")
species_IDD_with_40_threshold_indiv_per_volume = paste0(species_IDD_with_40_threshold, "_indiv_per_volume")
# Patches

patches_to_take_off = 60 #Culture ID = 60 as it was spilled (small isolated, high disturbance, system nr = 40)
culture_info = read.csv(here("data", "culture_info.csv"), header = TRUE)

columns_patches = c("time_point",
                    "day",
                    "culture_ID",
                    "system_nr",
                    "disturbance",
                    "patch_type",
                    "patch_size",
                    "patch_size_ml",
                    "size_connected_patch",
                    "metaecosystem",
                    "metaecosystem_type")
columns_treatments = columns_patches[!columns_patches %in% c("system_nr", "culture_ID")]

variables_patches = c("bioarea_mm2_per_ml",
                      "bioarea_tot_mm2",
                      "indiv_per_ml",
                      "indiv_tot",
                      "species_richness",
                      "shannon",
                      "simpson",
                      "inv_simpson",
                      "evenness_pielou",
                      "median_body_area_µm2",
                      paste0(protist_species, "_indiv_per_ml"),
                      paste0(protist_species, "_tot_indiv"),
                      paste0(protist_species_indiv_per_ml, "_dominance"))
baseline_columns = paste0("baseline_", variables_patches)

patch_types_ordered = c("Small isolated",
                        "Small connected to small",
                        "Small connected to large",
                        "Medium isolated",
                        "Medium connected to medium",
                        "Large isolated",
                        "Large connected to large",
                        "Large connected to small")

treatments_and_controls = data.frame(
  treatment = c(
    "Small connected to small",
    "Small connected to large",
    "Medium connected to medium",
    "Large connected to large",
    "Large connected to small"
  ),
  control = c(
    "Small isolated",
    "Small isolated",
    "Medium isolated",
    "Large isolated",
    "Large isolated"
  )
)
n_treatments = length(unique(treatments_and_controls$treatment))
n_controls = length(unique(treatments_and_controls$control))
n_replicates = 5
n_patch_types = 8
# Size classes

n_size_classes = 12
columns_classes = c(columns_patches,
                    "size_class_n",
                    "mean_class_area_µm2")
# Meta-ecosystems

metaecosystems_to_take_off = culture_info %>%
  filter(culture_ID %in% patches_to_take_off) %>%
  pull(system_nr) %>%
  unique

system_nr_metaecosystems = culture_info %>%
  filter(metaecosystem == "yes") %>%
  pull(system_nr) %>%
  unique

n_metaecosystems = length(system_nr_metaecosystems)

variables_metaecos = c(
  "total_metaecosystem_bioarea_mm2",
  "jaccard_index",
  "bray_curtis",
  "beta_spatial_turnover",
  "beta_nestedness",
  "beta_total",
  "metaecosystem_richness")

metaecosystem_types_ordered = c(
      "Small-Small meta-ecosystem",
      "Medium-Medium meta-ecosystem",
      "Medium-Medium isolated",
      "Large-Large meta-ecosystem",
      "Small-Large meta-ecosystem",
      "Small-Large isolated")
# Plotting

axis_names = data.frame(variable = NA,
                        axis_name= NA) %>%
  add_row(variable = "day", axis_name = "Time (day)") %>%
  add_row(variable = "patch_size_ml", axis_name = "Patch size (ml)") %>%
  add_row(variable = "log_size_class", axis_name = "Log size (μm2)") %>%
  add_row(variable = "class_indiv_per_µl", axis_name = "Density (ind/ml)") %>%
  add_row(variable = "bioarea_mm2_per_ml", axis_name = "Bioarea density (mm²/ml)") %>%
  add_row(variable = "bioarea_mm2_per_ml_d", axis_name = "Bioarea Density ES") %>%
  add_row(variable = "bioarea_tot", axis_name = "Tot Bioarea (mm²)") %>%
  add_row(variable = "total_metaecosystem_bioarea_mm2", axis_name = "Tot Bioarea (mm²)") %>%
  add_row(variable = "species_richness", axis_name = "Species Richness") %>%
  add_row(variable = "species_richness_d", axis_name = "Species Richness ES") %>%
  add_row(variable = "mean_richness", axis_name = "mean α Diversity (Shannon)") %>%
  add_row(variable = "mean_shannon", axis_name = "mean α Diversity (Shannon)") %>%
  add_row(variable = "shannon", axis_name = "Biodiversity (Shannon)") %>%
  add_row(variable = "shannon_d", axis_name = "Biodiversity ES (Shannon ES)") %>%
  add_row(variable = "bray_curtis", axis_name = "β Diversity (Bray-Curtis)") %>%
  add_row(variable = "beta_spatial_turnover", axis_name = "Turn over (Simpson pair-wise dissimilarity)") %>%
  add_row(variable = "beta_nestedness", axis_name = "Nestedness (nestedness-fraction of Sorensen)") %>%
  add_row(variable = "beta_total", axis_name = "Tot β Diversity (Sorensen)") %>%
  add_row(variable = "metaecosystem_richness", axis_name = "γ Diversity (Species Richness)") %>%
  add_row(variable = "indiv_per_ml", axis_name = "Abundance (ind/ml)") %>%
  add_row(variable = "indiv_per_ml_d", axis_name = "Abundance ES") %>%
  add_row(variable = "median_body_area_µm2", axis_name = "Median body size (µm²)") %>%
  add_row(variable = "median_body_area_µm2_d", axis_name = "Median Body Size ES") %>%
  add_row(variable = "Ble_indiv_per_ml", axis_name = "Ble Density (ind/ml)") %>% 
  add_row(variable = "Cep_indiv_per_ml", axis_name = "Cep Density (ind/ml)") %>%
  add_row(variable = "Col_indiv_per_ml", axis_name = "Col Density (ind/ml)") %>%
  add_row(variable = "Eug_indiv_per_ml", axis_name = "Eug Density (ind/ml)") %>%
  add_row(variable = "Eup_indiv_per_ml", axis_name = "Eup Density (ind/ml)") %>%
  add_row(variable = "Lox_indiv_per_ml", axis_name = "Lox Density (ind/ml)") %>%
  add_row(variable = "Pau_indiv_per_ml", axis_name = "Pau Density (ind/ml)") %>%
  add_row(variable = "Pca_indiv_per_ml", axis_name = "Pca Density (ind/ml)") %>%
  add_row(variable = "Spi_indiv_per_ml", axis_name = "Spi Density (ind/ml)") %>%
  add_row(variable = "Spi_te_indiv_per_ml", axis_name = "Spi te Density (ind/ml)") %>%
  add_row(variable = "Tet_indiv_per_ml", axis_name = "Tet Density (ind/ml)") %>%
  add_row(variable = "Ble_indiv_per_ml_d", axis_name = "Ble Density ES") %>%
  add_row(variable = "Cep_indiv_per_ml_d", axis_name = "Cep Density ES") %>%
  add_row(variable = "Col_indiv_per_ml_d", axis_name = "Col Density ES") %>%
  add_row(variable = "Eug_indiv_per_ml_d", axis_name = "Eug Density ES") %>%
  add_row(variable = "Eup_indiv_per_ml_d", axis_name = "Eup Density ES") %>%
  add_row(variable = "Lox_indiv_per_ml_d", axis_name = "Lox Density ES") %>%
  add_row(variable = "Pau_indiv_per_ml_d", axis_name = "Pau Density ES") %>%
  add_row(variable = "Pca_indiv_per_ml_d", axis_name = "Pca Density ES") %>%
  add_row(variable = "Spi_indiv_per_ml_d", axis_name = "Spi Density ES") %>%
  add_row(variable = "Spi_te_indiv_per_ml_d", axis_name = "Spi te Density ES") %>%
  add_row(variable = "Tet_indiv_per_ml_d", axis_name = "Tet Density ES") %>%
  add_row(variable = "Ble_indiv_per_ml_dominance", axis_name = "Ble Dominance (%)") %>%
  add_row(variable = "Cep_indiv_per_ml_dominance", axis_name = "Cep Dominance (%)") %>%
  add_row(variable = "Col_indiv_per_ml_dominance", axis_name = "Col Dominance (%)") %>%
  add_row(variable = "Eug_indiv_per_ml_dominance", axis_name = "Eug Dominance (%)") %>%
  add_row(variable = "Eup_indiv_per_ml_dominance", axis_name = "Eup Dominance (%)") %>%
  add_row(variable = "Lox_indiv_per_ml_dominance", axis_name = "Lox Dominance (%)") %>%
  add_row(variable = "Pau_indiv_per_ml_dominance", axis_name = "Pau Dominance (%)") %>%
  add_row(variable = "Pca_indiv_per_ml_dominance", axis_name = "Pca Dominance (%)") %>%
  add_row(variable = "Spi_indiv_per_ml_dominance", axis_name = "Spi Dominance (%)") %>%
  add_row(variable = "Spi_te_indiv_per_ml_dominance", axis_name = "Spi te Dominance (%)") %>%
  add_row(variable = "Tet_indiv_per_ml_dominance", axis_name = "Tet Dominance (%)") %>%
  add_row(variable = "Ble_indiv_per_ml_dominance_d", axis_name = "Ble Dominance ES") %>%
  add_row(variable = "Cep_indiv_per_ml_dominance_d", axis_name = "Cep Dominance ES") %>%
  add_row(variable = "Col_indiv_per_ml_dominance_d", axis_name = "Col Dominance ES") %>%
  add_row(variable = "Eug_indiv_per_ml_dominance_d", axis_name = "Eug Dominance ES") %>%
  add_row(variable = "Eup_indiv_per_ml_dominance_d", axis_name = "Eup Dominance ES") %>%
  add_row(variable = "Lox_indiv_per_ml_dominance_d", axis_name = "Lox Dominance ES") %>%
  add_row(variable = "Pau_indiv_per_ml_dominance_d", axis_name = "Pau Dominance ES") %>%
  add_row(variable = "Pca_indiv_per_ml_dominance_d", axis_name = "Pca Dominance ES") %>%
  add_row(variable = "Sp_indiv_per_mli_dominance_d", axis_name = "Spi Dominance ES") %>%
  add_row(variable = "Spi_te_indiv_per_ml_dominance_d", axis_name = "Spi te Dominance ES") %>%
  add_row(variable = "Tet_indiv_per_ml_dominance_d", axis_name = "Tet Dominance ES") %>%
  add_row(variable = "dominance", axis_name = "Dominance (%)") %>%
  add_row(variable = "log_abundance", axis_name = "Log Abundance + 1 (ind/mm²)") %>%
  add_row(variable = "abundance_hedges_d", axis_name = "Density ES") %>%
  add_row(variable = "beta_diversity_from_isolated", axis_name = "Divergence from isolated") %>%
  add_row(variable = "beta_diversity_from_previous_time", axis_name = "Temporal Divergence") %>%
  add_row(variable = "beta_diversity_from_previous_time_d", axis_name = "Temporal Divergence ES") %>%
  add_row(variable = "evenness_pielou", axis_name = "Evenness") %>%
  add_row(variable = "evenness_pielou_d", axis_name = "Evenness ES") %>%
  slice(-1)

parameters_treatments = data.frame(treatment = NA,
                                colour = NA,
                                linetype = NA) %>%
  add_row(treatment = "Small isolated", colour = "#bdbdbd", linetype = "solid") %>% 
  add_row(treatment = "Small connected to small", colour = "#feb24c", linetype = "dashed") %>%
  add_row(treatment = "Small connected to large", colour = "#feb24c", linetype = "solid") %>%
  add_row(treatment = "Medium isolated", colour = "#737373", linetype = "solid") %>%
  add_row(treatment = "Medium connected to medium", colour = "#de2d26", linetype = "solid") %>%
  add_row(treatment = "Large isolated", colour = "#000000", linetype = "solid") %>%
  add_row(treatment = "Large connected to large", colour = "#3182bd", linetype = "dashed") %>%
  add_row(treatment = "Large connected to small", colour = "#3182bd", linetype = "solid") %>%
  add_row(treatment = "Small-Small meta-ecosystem", colour = "#fc9272", linetype = "solid") %>%
  add_row(treatment = "Medium-Medium meta-ecosystem", colour = "#1b7837", linetype = "solid") %>%
  add_row(treatment = "Medium-Medium isolated", colour = "#1b7837", linetype = "dashed") %>%
  add_row(treatment = "Large-Large meta-ecosystem", colour = "#67000d", linetype = "solid") %>%
  add_row(treatment = "Small-Large meta-ecosystem", colour = "#762a83", linetype = "solid") %>%
  add_row(treatment = "Small-Large isolated", colour = "#762a83", linetype = "dashed") %>%
  slice(-1)

figures_height_rmd_output = 7

legend_position = "top"
legend_width_cm = 2
size_legend = 12
size_x_axis = 13
size_y_axis = size_x_axis
boxplot_width = 2
dodging = 0.5 
width_errorbar = 0.2
dodging_error_bar = 0.5

treatment_lines_linewidth = 1
treatment_points_size = 2.5

resource_flow_line_type = "solid"
resource_flow_line_colour = "#d9d9d9"
resource_flow_line_width = 0.3

zero_line_colour = "grey"
zero_line_line_type = "dotted"
zero_line_line_width = 0.5
zero_line_ES_line_type = "dotted"
zero_line_ES_colour = "grey"
zero_line_ES_line_width = 1

ggarrange_margin_top = 0
ggarrange_margin_bottom = 0
ggarrange_margin_left = 0
ggarrange_margin_right = 0

paper_width = 17.3
paper_height = 20
paper_units = "cm"
paper_res = 600

presentation_figure_size = 15
presentation_figure_width = 30
presentation_figure_height = 22
presentation_legend_size = 20
presentation_x_axis_size = 20
presentation_y_axis_size = presentation_x_axis_size
presentation_axes_size = 12
presentation_treatment_points_size = 5
presentation_treatment_linewidth = 2
presentation_figure_units = "cm"
presentation_figure_res = 600
# Modelling

first_time_point_model = 2 #Time point 2 is the first after disturbance
last_time_point_model = last_time_point

optimizer_input = 'Nelder_Mead'
method_input = ''

compar_ex = "The full and fixed models are compared to the null model."

results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA
) %>%
  slice(-1)

Data

Experimental cultures (culture_info)

This table contains information about the 110 cultures of the experiment.

# Import culture informations

culture_info = read.csv(here("data", "culture_info.csv"), header = TRUE)

Individuals (ds_individuals)

In this dataset (ds_individuals) each row represents an individual at a time point.

# Import the individual data of t0. We considered cultures to be all the same at the beginning (t0). Because of this reason, we filmed only the bottles from which cultures were assembled. Because we want to plot also t0 for the different treatments, we want to assign the video of bottles to all cultures at t0.

ds_individuals_t0_not_elongated = read.csv(here("data", "individuals", "t0.csv")) %>%
  mutate(time_point =  as.numeric(str_extract(time_point, "\\d+")),
         day = 0,
         file =  as.numeric(str_extract(file, "\\d+")),
         video_replicate = file) %>%
  select(time_point,
         day,
         video_replicate,
         file,
         id,
         N_frames,
         mean_area)

ds_individuals_t0_elongated = ds_individuals_t0_not_elongated %>%
  map_dfr(.x = 1 : nrow(culture_info),
          .f = ~ ds_individuals_t0_not_elongated) %>% 
  arrange(id) %>% #Id refers to an individual
  mutate(culture_ID = rep(1 : nrow(culture_info),
                          times = nrow(ds_individuals_t0_not_elongated))) %>%
  select(time_point,
         day,
         video_replicate,
         file,
         culture_ID,
         id,
         N_frames,
         mean_area)

expect_equal(nrow(ds_individuals_t0_not_elongated) * nrow(culture_info),
             nrow(ds_individuals_t0_elongated))
#Import t1-t4

ds_individuals_t1_to_t4 = NULL
  
for (time_point_i in time_points_without_t0) {
  
  ds_individuals_t1_to_t4[[time_point_i]] = read.csv(here("data", 
                                                              "individuals", 
                                                              paste0("t", 
                                                                     time_point_i, 
                                                                     ".csv"))) %>%
    mutate(time_point =  as.numeric(str_extract(time_point, "\\d+")),
           day = time_point_day$day[time_point_day$time_point == time_point_i],
           file =  as.numeric(str_extract(file, "\\d+")),
           video_replicate = ceiling(file/n_cultures)) #Until 110 video replicate = 1, then 2
  }

ds_individuals_t1_to_t4 = ds_individuals_t1_to_t4 %>%
  bind_rows() %>%
  select(time_point,
         day,
         video_replicate,
         file,
         culture_ID,
         id,
         N_frames,
         mean_area)
# Bind t0 with t1-t4

ds_individuals = rbind(ds_individuals_t0_elongated,
                       ds_individuals_t1_to_t4) %>%
  left_join(culture_info,
            by = "culture_ID") 
# Rename and select columns

ds_individuals = ds_individuals %>% 
  select(
    disturbance,
    disturbance_volume,
    time_point,
    day,
    video_replicate,
    culture_ID,
    system_nr,
    file,
    eco_metaeco_type,
    patch_size,
    patch_size_volume,
    metaecosystem,
    metaecosystem_type,
    mean_area,
    N_frames
  ) %>%
  rename(patch_size_ml = patch_size_volume,
         patch_type = eco_metaeco_type,
         body_area_µm2 = mean_area)
# Rename and reorder levels

ds_individuals <- ds_individuals %>%
  mutate(patch_type = case_when(patch_type == "S" ~ "Small isolated",
                                patch_type == "M" ~ "Medium isolated",
                                patch_type == "L" ~ "Large isolated",
                                patch_type == "S (S_S)" ~ "Small connected to small",
                                patch_type == "S (S_L)" ~ "Small connected to large",
                                patch_type == "M (M_M)" ~ "Medium connected to medium",
                                patch_type == "L (S_L)" ~ "Large connected to small",
                                patch_type == "L (L_L)" ~ "Large connected to large",
                                TRUE ~ patch_type),
         patch_type = factor(patch_type,
                             levels = patch_types_ordered))

ds_individuals <- ds_individuals %>%
  mutate(patch_size = case_when(patch_size == "S" ~ "Small",
                                patch_size == "M" ~ "Medium",
                                patch_size == "L" ~ "Large",
                                TRUE ~ patch_type),
         patch_size = factor(patch_size,
                             levels = "Small", 
                                      "Medium", 
                                      "Large"))

ds_individuals <- ds_individuals %>%
  mutate(size_connected_patch = case_when(patch_type == "Small connected to small" ~ "Small",
                                          patch_type == "Small connected to large" ~ "Large",
                                          patch_type == "Medium connected to medium" ~ "Medium",
                                          patch_type == "Large connected to large" ~ "Large",
                                          patch_type == "Large connected to small" ~ "Small",
                                          TRUE ~ NA_character_))
# Take off problematic videos

ds_individuals_before_taking_off_videos = ds_individuals

ds_individuals = ds_individuals %>%
  filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))

diff = setdiff(ds_individuals_before_taking_off_videos, ds_individuals)

expect_equal(nrow(videos_to_take_off),
             nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))
# Take off problematic cultures 

ds_individuals_before_taking_off_cultures = ds_individuals

ds_individuals = ds_individuals %>%
  filter(!culture_ID %in% patches_to_take_off)

expect_equal(setdiff(ds_individuals_before_taking_off_cultures, 
                     ds_individuals) %>% 
               pull(culture_ID) %>% 
               unique(),
             patches_to_take_off)

Patches (ds_patches)

In this dataset (ds_patches) each row represents a patch at a time point. I use the data from the 40 threshold analysis for Ble, Cep, Spi and the data from the 13 threshold analysis for all the other protists (Col, Eup, Lox, Pau, Pca, Spi te, Tet).

# Import & bind t0 datasets.

ds_patches_t0 = read.csv(here("data", "populations", "t0.csv")) %>%
  mutate(time_point =  as.numeric(str_extract(time_point, "\\d+")),
         day = 0,
         video_replicate = file) %>%
  select(time_point,
         day,
         video_replicate,
         file,
         bioarea_per_volume,
         indiv_per_volume)

species_ID_13_threshold_t0 = read.csv(here("data", "species_ID_13_threshold", paste0("t0.csv"))) %>%
  rename(Ble_indiv_per_volume = Ble,
         Cep_indiv_per_volume = Cep,
         Col_indiv_per_volume = Col,
         Eug_indiv_per_volume = Eug,
         Eup_indiv_per_volume = Eup,
         Lox_indiv_per_volume = Lox,
         Pau_indiv_per_volume = Pau,
         Pca_indiv_per_volume = Pca,
         Spi_indiv_per_volume = Spi,
         Spi_te_indiv_per_volume = Spi_te,
         Tet_indiv_per_volume = Tet) %>%
  select(file,
         all_of(species_IDD_with_13_threshold_indiv_per_volume))

species_ID_40_threshold_t0 = read.csv(here("data", "species_ID_40_threshold", paste0("t0.csv"))) %>%
  rename(Ble_indiv_per_volume = Ble,
         Cep_indiv_per_volume = Cep,
         Col_indiv_per_volume = Col,
         Eug_indiv_per_volume = Eug,
         Eup_indiv_per_volume = Eup,
         Lox_indiv_per_volume = Lox,
         Pau_indiv_per_volume = Pau,
         Pca_indiv_per_volume = Pca,
         Spi_indiv_per_volume = Spi,
         Spi_te_indiv_per_volume = Spi_te,
         Tet_indiv_per_volume = Tet) %>%
  select(file,
         all_of(species_IDD_with_40_threshold_indiv_per_volume))

ds_patches_t0 = ds_patches_t0 %>%
  left_join(species_ID_13_threshold_t0,
            by = "file") %>%
  left_join(species_ID_40_threshold_t0,
            by = "file") %>%
  mutate(file =  as.numeric(str_extract(file, "\\d+")))
# Elongate t0 dataset.

ds_patches_t0_elongated <- list()

for (video_i in 1 : n_videos_taken_t0) {
  
  single_video = ds_patches_t0 %>%
    filter(file == video_i)
  
  ds_patches_t0_elongated[[video_i]] = culture_info %>%
    mutate(
      time_point = 0,
      day = 0,
      file = single_video$file,
      video_replicate = single_video$video_replicate,
      bioarea_per_volume = single_video$bioarea_per_volume,
      indiv_per_volume = single_video$indiv_per_volume,
      Ble_indiv_per_volume = single_video$Ble_indiv_per_volume,
      Cep_indiv_per_volume = single_video$Cep_indiv_per_volume,
      Col_indiv_per_volume = single_video$Col_indiv_per_volume,
      Eug_indiv_per_volume = single_video$Eug_indiv_per_volume,
      Eup_indiv_per_volume = single_video$Eup_indiv_per_volume,
      Lox_indiv_per_volume = single_video$Lox_indiv_per_volume,
      Pau_indiv_per_volume = single_video$Pau_indiv_per_volume,
      Pca_indiv_per_volume = single_video$Pca_indiv_per_volume,
      Spi_indiv_per_volume = single_video$Spi_indiv_per_volume,
      Spi_te_indiv_per_volume = single_video$Spi_te_indiv_per_volume,
      Tet_indiv_per_volume = single_video$Tet_indiv_per_volume)
}

ds_patches_t0_elongated = ds_patches_t0_elongated %>%
  bind_rows()
# Clean the columns of t0

ds_patches_t0 = ds_patches_t0_elongated %>%
  select(
    file,
    time_point,
    day,
    culture_ID,
    video_replicate,
    bioarea_per_volume,
    indiv_per_volume,
    all_of(protist_species_indiv_per_volume))

expect_equal(nrow(ds_patches_t0), sum(n_videos_taken_t0 * n_cultures))
# Import and bind t1-t4

ds_patches_t1_to_t4 = NULL

for (time_point_i in time_points_without_t0) {
  
  species_ID_13_threshold = read.csv(here("data", 
                                          "species_ID_13_threshold", 
                                          paste0("t", time_point_i, ".csv"))) %>%
    rename(Ble_indiv_per_volume = Ble,
         Cep_indiv_per_volume = Cep,
         Col_indiv_per_volume = Col,
         Eug_indiv_per_volume = Eug,
         Eup_indiv_per_volume = Eup,
         Lox_indiv_per_volume = Lox,
         Pau_indiv_per_volume = Pau,
         Pca_indiv_per_volume = Pca,
         Spi_indiv_per_volume = Spi,
         Spi_te_indiv_per_volume = Spi_te,
         Tet_indiv_per_volume = Tet) %>%
    select(file,
           all_of(species_IDD_with_13_threshold_indiv_per_volume))
  
  
  species_ID_40_threshold = read.csv(here("data", 
                                          "species_ID_40_threshold", 
                                          paste0("t", time_point_i, ".csv"))) %>%
    rename(Ble_indiv_per_volume = Ble,
         Cep_indiv_per_volume = Cep,
         Col_indiv_per_volume = Col,
         Eug_indiv_per_volume = Eug,
         Eup_indiv_per_volume = Eup,
         Lox_indiv_per_volume = Lox,
         Pau_indiv_per_volume = Pau,
         Pca_indiv_per_volume = Pca,
         Spi_indiv_per_volume = Spi,
         Spi_te_indiv_per_volume = Spi_te,
         Tet_indiv_per_volume = Tet) %>%
    select(file,
           all_of(species_IDD_with_40_threshold_indiv_per_volume))
  
  
  ds_patches_t1_to_t4[[time_point_i]] = read.csv(here("data", 
                                                      "populations", 
                                                      paste0("t", time_point_i, ".csv"))) %>%
    arrange(file) %>%
    mutate(video_replicate = rep(1 : time_point_day$video_replicates[time_point_i+1],
                                 each = n_cultures),
           day = time_point_day$day[time_point_day$time_point == time_point_i]) %>%
    select(
      file,
      time_point,
      day,
      video_replicate,
      file,
      culture_ID,
      bioarea_per_volume,
      indiv_per_volume)
  
  ds_patches_t1_to_t4[[time_point_i]] = ds_patches_t1_to_t4[[time_point_i]] %>%
    left_join(species_ID_13_threshold,
                by = "file") %>%
    left_join(species_ID_40_threshold,
                by = "file")
    
}

ds_patches_t1_to_t4 = ds_patches_t1_to_t4 %>%
  bind_rows()
# Bind t0 with t1-t4

ds_patches = rbind(ds_patches_t0,
                   ds_patches_t1_to_t4) %>%
             left_join(culture_info,
                       by = "culture_ID") 
    
expect_equal(nrow(ds_patches),
             sum(sum(time_point_day$video_replicates) * n_cultures))
# Reorder and rename columns

ds_patches = ds_patches %>%
  rename(patch_size_ml = patch_size_volume) %>%
  select(
    file,
    time_point,
    day,
    disturbance,
    culture_ID,
    system_nr,
    eco_metaeco_type,
    patch_size,
    patch_size_ml,
    metaecosystem,
    metaecosystem_type,
    video_replicate,
    bioarea_per_volume,
    indiv_per_volume,
    all_of(protist_species_indiv_per_volume)
  ) %>%
  rename(patch_type = eco_metaeco_type,
         bioarea_µm2_per_μL = bioarea_per_volume) %>%
  rename_all( ~ gsub("volume", "μL", .))
# Rename and reorder levels

ds_patches <- ds_patches %>%
  mutate(
    patch_type = case_when(
      patch_type == "S" ~ "Small isolated",
      patch_type == "M" ~ "Medium isolated",
      patch_type == "L" ~ "Large isolated",
      patch_type == "S (S_S)" ~ "Small connected to small",
      patch_type == "S (S_L)" ~ "Small connected to large",
      patch_type == "M (M_M)" ~ "Medium connected to medium",
      patch_type == "L (S_L)" ~ "Large connected to small",
      patch_type == "L (L_L)" ~ "Large connected to large",
      TRUE ~ patch_type
    ),
    patch_type = factor(x = patch_type,
                        levels = patch_types_ordered),
    patch_size = case_when(
      patch_size == "S" ~ "Small",
      patch_size == "M" ~ "Medium",
      patch_size == "L" ~ "Large",
      TRUE ~ patch_size
    ),
    metaecosystem_type = case_when(
      metaecosystem_type == "S_S" ~ "Small-Small meta-ecosystem",
      metaecosystem_type == "M_M" ~ "Medium-Medium meta-ecosystem",
      metaecosystem_type == "L_L" ~ "Large-Large meta-ecosystem",
      metaecosystem_type == "S_L" ~ "Small-Large meta-ecosystem",
      TRUE ~ metaecosystem_type
    ),
    size_connected_patch = case_when(
      patch_type == "Small connected to small" ~ "Small",
      patch_type == "Small connected to large" ~ "Large",
      patch_type == "Medium connected to medium" ~ "Medium",
      patch_type == "Large connected to large" ~ "Large",
      patch_type == "Large connected to small" ~ "Small",
      TRUE ~ NA_character_
      ),
    time_point =  as.numeric(str_extract(time_point, "\\d+")),
    file =  as.numeric(str_extract(file, "\\d+")),
  )
# Change units of measurments to ml

ds_patches = ds_patches %>%
  mutate(bioarea_µm2_per_ml = bioarea_µm2_per_μL * 10^3,
         bioarea_mm2_per_ml = bioarea_µm2_per_ml * 10^(-6),
         Ble_indiv_per_ml = Ble_indiv_per_μL * 10^3,
         Cep_indiv_per_ml = Cep_indiv_per_μL * 10^3,
         Col_indiv_per_ml = Col_indiv_per_μL * 10^3,
         Eug_indiv_per_ml = Eug_indiv_per_μL * 10^3,
         Eup_indiv_per_ml = Eup_indiv_per_μL * 10^3,
         Lox_indiv_per_ml = Lox_indiv_per_μL * 10^3,
         Pau_indiv_per_ml = Pau_indiv_per_μL * 10^3,
         Pca_indiv_per_ml = Pca_indiv_per_μL * 10^3,
         Spi_indiv_per_ml = Spi_indiv_per_μL * 10^3,
         Spi_te_indiv_per_ml = Spi_te_indiv_per_μL * 10^3,
         Tet_indiv_per_ml = Tet_indiv_per_μL * 10^3)
# Take off problematic videos

ds_patches_before_taking_off_videos = ds_patches

ds_patches = ds_patches %>%
  filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))

diff = setdiff(ds_patches_before_taking_off_videos, ds_patches)

expect_equal(nrow(videos_to_take_off),
             nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))
# Take off problematic cultures 

ds_patches_before_taking_off_cultures = ds_patches

ds_patches = ds_patches %>%
  filter(!culture_ID %in% patches_to_take_off)

expect_equal(setdiff(ds_patches_before_taking_off_cultures, 
                     ds_patches) %>% 
               pull(culture_ID) %>% 
               unique(),
             patches_to_take_off)
# Average videos

ds_patches = ds_patches %>%
  group_by(across(all_of(columns_patches))) %>%
  summarise(across(contains("_per_ml"), mean),
            across(contains("_tot"), mean)) %>%
  ungroup()

expect_equal(nrow(ds_patches), 
             (n_cultures - length(patches_to_take_off)) * length(time_points))
# Add connection and individuals

ds_patches = ds_patches %>%
  mutate(indiv_per_ml = !!rlang::parse_expr(paste(protist_species_indiv_per_ml, 
                                                  collapse = " + ")))

ds_patches = ds_patches %>%
  mutate(connection = ifelse(str_detect(patch_type, "isolated"), 
                             "isolated", 
                             "connected"),
         connection = factor(x = connection,
                        levels = c("isolated", "connected")))
# Calculate total response variable for the whole patch

ds_patches = ds_patches %>%
  mutate(bioarea_tot_mm2 = bioarea_mm2_per_ml * patch_size_ml,
         indiv_tot = indiv_per_ml * patch_size_ml,
         Ble_tot_indiv = Ble_indiv_per_ml * patch_size_ml,
         Cep_tot_indiv = Cep_indiv_per_ml * patch_size_ml,
         Col_tot_indiv = Col_indiv_per_ml * patch_size_ml,
         Eug_tot_indiv = Eug_indiv_per_ml * patch_size_ml,
         Eup_tot_indiv = Eup_indiv_per_ml * patch_size_ml,
         Lox_tot_indiv = Lox_indiv_per_ml * patch_size_ml,
         Pau_tot_indiv = Pau_indiv_per_ml * patch_size_ml,
         Pca_tot_indiv = Pca_indiv_per_ml * patch_size_ml,
         Spi_tot_indiv = Spi_indiv_per_ml * patch_size_ml,
         Spi_te_tot_indiv = Spi_te_indiv_per_ml * patch_size_ml,
         Tet_tot_indiv = Tet_indiv_per_ml * patch_size_ml)
# Calculate species dominance

ds_patches = ds_patches %>%
  mutate(across(.cols = all_of(protist_species_indiv_per_ml), 
                .fns = list(dominance = ~ (. / indiv_per_ml) * 100), 
                .names = "{col}_dominance"))

expect_equal(unique(ds_patches$Ble_indiv_per_ml_dominance[ds_patches$indiv_per_ml == 0]), NaN)

if (FALSE %in% unique((ds_patches$Ble_indiv_per_ml/ds_patches$indiv_per_ml) *100 == ds_patches$Ble_indiv_per_ml_dominance)) stop()
# Calculate alpha diversity (Shannon, Simpson, Inverse Simpson, Evenness)

n_rows_ds_patches_before_calculating_alpha = nrow(ds_patches)

ds_patches = calculate.alpha.diversity()

expect_equal(max(ds_patches$species_richness), 
             length(protist_species))

expect_equal(nrow(ds_patches),
             n_rows_ds_patches_before_calculating_alpha)
# Calculate median body size

n_rows_ds_patches_before_median_size = nrow(ds_patches)

ds_median_body_size = ds_individuals %>%
  group_by(time_point,
           culture_ID,
           file) %>%
  summarise(median_body_area_µm2 = median(body_area_µm2)) %>%
  group_by(time_point,
           culture_ID) %>%
  summarise(median_body_area_µm2 = mean(median_body_area_µm2))

expect_true(nrow(ds_median_body_size) <= nrow(ds_patches)) #Ds median body size could be less because some cultures might be crashed and not have any individual.

ds_patches_before_full_join = ds_patches

ds_patches = full_join(ds_patches, ds_median_body_size)

expect_equal(nrow(ds_patches), 
             n_rows_ds_patches_before_median_size)

Patch effect sizes (ds_patches_effect_size)

In this dataset (ds_patches_effect_size) each row represents a treatment at a time point. It contains the effect size of the connection of a patch (connected vs isolated).

# Calculate the mean & sd of response variables for each treatment/control at each time point

ds_patches_effect_size = NULL
variable_nr = 0

for (variable_i in variables_patches) {
  
  variable_nr = variable_nr + 1
  
  ds_patches_effect_size[[variable_nr]] = ds_patches %>%
    filter(time_point >= 1,
           !is.na(!!sym(variable_i))) %>%
    group_by(across(all_of(columns_patches[columns_patches != "culture_ID" & 
                                           columns_patches != "system_nr"]))) %>%
    summarise(across(all_of(variable_i),
                     list(mean = mean,
                          sd = sd)),
              sample_size = n()) %>%
    rename_with( ~ paste0(variable_i, "_sample_size"),
                 matches("sample_size"))
  
}

ds_patches_effect_size <- reduce(ds_patches_effect_size,
                                 full_join,
                                 by = columns_patches[columns_patches != "culture_ID" &
                                                        columns_patches != "system_nr"])

expect_equal(nrow(ds_patches_effect_size),
             n_patch_types * (n_time_points-1) * n_disturbance_levels)
# Calculate the effect size (Hedge's d) for each treatment at each time point
  
  for (variable_i in variables_patches) {
    ds_patches_effect_size <- ds_patches_effect_size %>%
      mutate(
        !!paste0(variable_i, "_d") := NA,
        !!paste0(variable_i, "_d_upper") := NA,
        !!paste0(variable_i, "_d_lower") := NA
      )
  }
  
  row_n = 0
  for (treatment_input in treatments_and_controls$treatment) {
    for (time_point_input in time_points) {
      row_n = row_n + 1
      
      control_input = treatments_and_controls$control[
        treatments_and_controls$treatment == treatment_input
        ]
      
      treatment_row = ds_patches_effect_size %>%
        filter(patch_type == treatment_input,
               time_point == time_point_input)
      
      control_row = ds_patches_effect_size %>%
        filter(patch_type == control_input,
               time_point == time_point_input)
      
      for (response_variable in variables_patches) {
        
        hedges_d = calculate.hedges_d(
          treatment_row[[paste0(response_variable, "_mean")]],
          treatment_row[[paste0(response_variable, "_sd")]],
          treatment_row[[paste0(response_variable, "_sample_size")]],
          control_row[[paste0(response_variable, "_mean")]],
          control_row[[paste0(response_variable, "_sd")]],
          control_row[[paste0(response_variable, "_sample_size")]]
        )
        
        ds_patches_effect_size[[paste0(response_variable, "_d")]][
          ds_patches_effect_size$patch_type == treatment_input &
          ds_patches_effect_size$time_point == time_point_input] =
          hedges_d$d
        
        ds_patches_effect_size[[paste0(response_variable, "_d_upper")]][
          ds_patches_effect_size$patch_type == treatment_input &
          ds_patches_effect_size$time_point == time_point_input] =
          hedges_d$upper_CI
        
        ds_patches_effect_size[[paste0(response_variable, "_d_lower")]][
          ds_patches_effect_size$patch_type == treatment_input &
          ds_patches_effect_size$time_point == time_point_input] =
          hedges_d$lower_CI
        
      }
    }
  }

expect_equal(nrow(ds_patches_effect_size),
             n_patch_types * (n_time_points-1) * n_disturbance_levels)

Meta-ecosystems (ds_metaecosystems)

In this dataset (ds_metaecosystems) each row represents a meta-ecosystem or a two-patch isolated system at a time point.

#Find combinations of patches in isolated two-patch systems.

ID_isolated_S_low = ds_patches %>%
  filter(patch_type == "Small isolated",
         disturbance == "low") %>%
  pull(culture_ID) %>%
  unique()

ID_isolated_M_low = ds_patches %>%
  filter(patch_type == "Medium isolated",
         disturbance == "low") %>%
  pull(culture_ID) %>%
  unique()

ID_isolated_L_low = ds_patches %>%
  filter(patch_type == "Large isolated",
         disturbance == "low") %>%
  pull(culture_ID) %>%
  unique()

ID_isolated_S_high = ds_patches %>%
  filter(patch_type == "Small isolated",
         disturbance == "high") %>%
  pull(culture_ID) %>%
  unique()

ID_isolated_M_high = ds_patches %>%
  filter(patch_type == "Medium isolated",
         disturbance == "high") %>%
  pull(culture_ID) %>%
  unique()

ID_isolated_L_high = ds_patches %>%
  filter(patch_type == "Large isolated",
         disturbance == "high") %>%
  pull(culture_ID) %>%
  unique()
# Find combinations of patches to create different two-patch isolated systems

combinations_S_and_L_low = crossing(ID_isolated_S_low,
                                    ID_isolated_L_low) %>%
                            mutate(disturbance = "low",
                                   metaecosystem_type = "Small-Large isolated") %>%
                            rename(ID_first_patch = ID_isolated_S_low,
                                   ID_second_patch = ID_isolated_L_low) %>%
                            select(disturbance,
                                   metaecosystem_type,
                                   ID_first_patch,
                                   ID_second_patch)

combinations_S_and_L_high = crossing(ID_isolated_S_high,
                                     ID_isolated_L_high) %>%
                            mutate(disturbance = "high",
                                   metaecosystem_type = "Small-Large isolated") %>%
                            rename(ID_first_patch = ID_isolated_S_high,
                                   ID_second_patch = ID_isolated_L_high) %>%
                            select(disturbance,
                                   metaecosystem_type,
                                   ID_first_patch,
                                   ID_second_patch)

combinations_M_and_M_low = combinat::combn(ID_isolated_M_low,
                                   m = 2) %>%
                            t() %>%
                            as.data.frame() %>%
                            rename(ID_first_patch = V1,
                                   ID_second_patch = V2) %>%
                            mutate(disturbance = "low",
                                   metaecosystem_type = "Medium-Medium isolated") %>%
                            select(disturbance,
                                   metaecosystem_type,
                                   ID_first_patch,
                                   ID_second_patch)


combinations_M_and_M_high = combinat::combn(ID_isolated_M_high,
                                   m = 2) %>%
                            t() %>%
                            as.data.frame() %>%
                            rename(ID_first_patch = V1,
                                   ID_second_patch = V2) %>%
                            mutate(disturbance = "high",
                                   metaecosystem_type = "Medium-Medium isolated") %>%
                            select(disturbance,
                                   metaecosystem_type,
                                   ID_first_patch,
                                   ID_second_patch)
# Bind combinations

combinations_isolated_systems = rbind(combinations_S_and_L_low,
                                      combinations_S_and_L_high,
                                      combinations_M_and_M_low,
                                      combinations_M_and_M_high) %>%
  mutate(system_nr = 1001:(1000 + nrow(.))) %>%
  select(system_nr,
         disturbance,
         metaecosystem_type,
         ID_first_patch,
         ID_second_patch)
# Find combinations of patches that were connected to form meta-ecosystems.

combinations_metaecos = ds_patches %>%
  filter(time_point == 0,
         metaecosystem == "yes") %>%
  select(system_nr,
         disturbance,
         metaecosystem_type,
         culture_ID) %>%
  group_by(system_nr,
           disturbance,
           metaecosystem_type) %>%
  summarise(ID_first_patch = (mean(culture_ID) - 0.5),
            ID_second_patch = (mean(culture_ID) + 0.5)) %>%
  as.data.frame()
#Bind isolated two-patch systems and meta-ecosystems

patch_combinations = rbind(combinations_isolated_systems,
                           combinations_metaecos) %>%
  mutate(connection = ifelse(metaecosystem_type %in% c("Medium-Medium isolated",
                                                       "Small-Large isolated"),
                             yes = "isolated",
                             no = "connected"),
         patches_combined = paste0(ID_first_patch, "|", ID_second_patch))

n_patches_combinations = nrow(patch_combinations)
#Create sets for SL isolated, where in each set a small and a large patches are paired differently. To do so, I keep the small patches on the same order and perform permutations on large patches.

SL_isolated_sys_sets <- vector("list",
                               length(disturbance_levels))

for (disturbance_i in 1:length(disturbance_levels)) {
  
  ID_small_patches = ds_patches %>%
    filter(disturbance == disturbance_levels[disturbance_i],
           patch_type == "Small isolated") %>%
    pull(culture_ID) %>%
    unique()
  
  ID_large_patches = ds_patches %>%
    filter(disturbance == disturbance_levels[disturbance_i],
           patch_type == "Large isolated") %>%
    pull(culture_ID) %>%
    unique()
  
  #Force small and large patches vectors to have the same length
  length_difference <- length(ID_small_patches) - length(ID_large_patches)
  if (length_difference > 0) {
    
    ID_large_patches = c(ID_large_patches,
                       rep("Patch taken off",
                           times = abs(length(ID_small_patches) - 
                                       length(ID_large_patches))))
    
    } else if (length_difference < 0) {
      
    ID_small_patches = c(ID_small_patches,
                         rep("Patch taken off",
                             times = abs(length(ID_large_patches) - 
                                         length(ID_small_patches))))
    }

  # Create dataframe
  permutations_large = permn(ID_large_patches)
  
  SL_isolated_sys_sets[[disturbance_i]] = data.frame(
    disturbance = disturbance_levels[disturbance_i],
    metaecosystem_type = "Small-Large isolated",
    connection = "isolated",
    ID_first_patch = rep(ID_small_patches, times = length(permutations_large)),
    ID_second_patch = unlist(permutations_large),
    set = rep(1 : length(permutations_large), each = length(ID_small_patches)))
  
  expect_equal(nrow(SL_isolated_sys_sets[[disturbance_i]]),
               length(ID_small_patches) * length(permutations_large))
  
  SL_isolated_sys_sets[[disturbance_i]] = SL_isolated_sys_sets[[disturbance_i]] %>%
    filter(!ID_first_patch == "Patch taken off",
           !ID_second_patch == "Patch taken off") %>%
    mutate(ID_first_patch = as.double(ID_first_patch),
           ID_second_patch = as.double(ID_second_patch)) %>%
    full_join(patch_combinations %>%
                filter(disturbance == disturbance_levels[disturbance_i], 
                       metaecosystem_type == "Small-Large isolated")) #Add system_nr & patches_combined

}

SL_isolated_sys_sets_before_binding = SL_isolated_sys_sets
SL_isolated_sys_sets = SL_isolated_sys_sets %>%
  bind_rows()
expect_equal(nrow(SL_isolated_sys_sets),
             nrow(SL_isolated_sys_sets_before_binding[[1]]) + nrow(SL_isolated_sys_sets_before_binding[[2]]))

expect_equal(length(SL_isolated_sys_sets %>% 
                      pull(system_nr) %>% 
                      unique()),
             length(patch_combinations %>%
                      filter(metaecosystem_type == "Small-Large isolated") %>%
                      pull(system_nr) %>%
                      unique()))
#WORK HERE

#Create sets for MM isolated, where in each set two medium patches small are paired differently. To do so, I ... 

#Initialise MM_isolated_sets. Assign 10^4 rows to each matrix so that we have enough rows not to run out of them when we try to assign values to them. Assign 4 columns which will include culture_ID of the first system, second culture_ID of the fist system, culture_ID of the second system, and second culture_ID of the second system.  
MM_isolated_sets = NULL
for(disturbance_i in 1:length(disturbance_levels)){
  
  MM_isolated_sets[[disturbance_i]] <- matrix(nrow = 10 ^ 4, 
                                              ncol = 4)
  
}

for (disturbance_i in 1:length(disturbance_levels)) {
  
  ID_medium_patches = ds_patches %>%
    filter(disturbance == disturbance_levels[disturbance_i],
           patch_type == "Medium isolated") %>%
    pull(culture_ID) %>%
    unique()
  
  MM_isolated_systems = combn(ID_medium_patches, 
                              2) %>%
    t()
  
  matrix_row = 0
  for (first_system_i in 1:nrow(MM_isolated_systems)) {
    
    #Find culture IDs of the first system (what's the first system?)
    first_system = MM_isolated_systems[first_system_i, ]
    
    for (second_system_i in 1:nrow(MM_isolated_systems)) {
      
      #Find culture IDs of the second system (what's the second system?)
      second_system = MM_isolated_systems[second_system_i, ]
      
      shared_elements_among_systems = intersect(first_system,
                                                second_system)
      
      if (length(shared_elements_among_systems) == 0) {
        
        matrix_row = matrix_row + 1
        
        #Make first and second system into a set
        MM_isolated_sets[[disturbance_i]][matrix_row,] = c(first_system,
                                                            second_system)
        
        print(MM_isolated_sets[[disturbance_i]][matrix_row,])
        
      }
    }
  }
  
  #Tidy the dataset with all the patch combinations
  MM_isolated_sets[[disturbance_i]] = MM_isolated_sets[[disturbance_i]] %>%
    as.data.frame() %>%
    drop_na()
  
  expect_equal(MM_isolated_sets[[disturbance_i]] %>% 
                 filter(V1 == V2 | V1 == V3 | V1 == V4 | V2 == V3 | V2 == V4 | V3 == V4) %>% 
                 nrow(),
               0)
  
  #Reorder the dataset with all the patch combinations
  MM_isolated_sets_reordered = data.frame(ID_first_patch = NA,
                                          ID_second_patch = NA,
                                          set = NA)
  
  for (set_input in 1:nrow(MM_isolated_sets[[disturbance_i]])) {
    MM_isolated_sets_reordered = MM_isolated_sets_reordered %>%
      add_row(
        ID_first_patch = MM_isolated_sets[[disturbance_i]][set_input, 1],
        ID_second_patch = MM_isolated_sets[[disturbance_i]][set_input, 2],
        set = set_input
      ) %>%
      add_row(
        ID_first_patch = MM_isolated_sets[[disturbance_i]][set_input, 3],
        ID_second_patch = MM_isolated_sets[[disturbance_i]][set_input, 4],
        set = set_input
      )
  }
  
  #Add to a list
  MM_isolated_sets[[disturbance_i]] = MM_isolated_sets_reordered %>%
    drop_na() %>%
    mutate(
      disturbance = disturbance_levels[disturbance_i],
      metaecosystem_type = "Medium-Medium isolated",
      connection = "isolated"
    )
  
  #Add system nr
  ID_combinations_MM_isolated = patch_combinations %>%
    filter(disturbance == disturbance_levels[disturbance_i],
           metaecosystem_type == "Medium-Medium isolated")
  
  MM_isolated_sets[[disturbance_i]] = full_join(MM_isolated_sets[[disturbance_i]],
                                                     ID_combinations_MM_isolated)
  
}
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#Bind all sets of MM isolated
MM_isolated_sets = MM_isolated_sets %>%
  bind_rows()
expect_equal(length(MM_isolated_sets %>%
                      pull(system_nr) %>%
                      unique()),
             length(patch_combinations %>%
                      filter(metaecosystem_type == "Medium-Medium isolated") %>%
                      pull(system_nr) %>%
                      unique()))
#Bind SL and MM isolated systems

isolated_combinations_sets = rbind(SL_isolated_sys_sets, 
                                   MM_isolated_sets) %>%
  select(disturbance,
         metaecosystem_type,
         connection,
         set,
         system_nr,
         ID_first_patch,
         ID_second_patch)
  • Each row is a meta-ecosystem.

  • It contains also “fake” meta-ecosystems which I created from isolated patches (metaecosystem type = Small-Large isolated & metaecosystem type = Medium-Medium isolated).

  • Warning appear after the following code, as:

  1. alpha diversity is computed for a culture that is totally crashed, so it has not meaning.
  2. beta diversity is computed between a culture that is still alive and one that is totally crashed. Because one of the cultures is totally crashed, the Bray-Curtis index is 1.
#Compute meta-ecosystems  for each time point 

ds_metaecosystems = NULL
row_n = 0
for (combination in 1:n_patches_combinations) {
  for (time_point_input in time_points) {
    row_n = row_n + 1
    
    current_day = sampling_days[time_point_input + 1]
    current_system_nr = patch_combinations[combination, ]$system_nr
    current_combination = patch_combinations[combination, ]$patches_combined
    current_disturbance = patch_combinations[combination, ]$disturbance
    current_metaeco_type = patch_combinations[combination, ]$metaecosystem_type
    current_IDs = c(patch_combinations[combination, ]$ID_first_patch,
                    patch_combinations[combination, ]$ID_second_patch)
    
    if (current_system_nr %in% metaecosystems_to_take_off)
      next
    
    if (current_IDs[1] == current_IDs[2])
      next
    
    species_vector_two_patches = ds_patches %>%
      filter(time_point == time_point_input,
             culture_ID %in% current_IDs) %>%
      ungroup() %>%
      select(all_of(protist_species_indiv_per_ml))
    
    absence_presence_two_patches <-
      ifelse(species_vector_two_patches > 0, 1, 0)
    
    #Alpha diversity: Shannon (mean between the two patches)
    shannon_patch_1 = diversity(species_vector_two_patches[1, ], index = "shannon")
    shannon_patch_2 = diversity(species_vector_two_patches[2, ], index = "shannon")
    shannon_value = (shannon_patch_1 + shannon_patch_2) / 2
    
    #Alpha diversity: Species richness (mean between the two patches)
    richness_patch_1 = specnumber(species_vector_two_patches[1, ])
    richness_patch_2 = specnumber(species_vector_two_patches[2, ])
    mean_richness_value = (richness_patch_1 + richness_patch_2) / 2
    
    #Beta diversity: Jaccard
    jaccard_index_value = vegdist(species_vector_two_patches,
                                  method = "jaccard") %>%
      as.numeric()
    
    #Beta diversity: Bray Curtis
    bray_curtis_value = vegdist(species_vector_two_patches,
                                method = "bray") %>%
      as.numeric()
    
    #Beta diversity: partitioning of beta diversity from Sorensen index into turnover (Simpson pair-wise dissimilarity) and nestedness (nestedness-fraction of Sorensen)
    betapart_core_object = betapart.core(absence_presence_two_patches)
    beta_spatial_turnover_value = beta.pair(betapart_core_object)$beta.sim %>% as.double()
    beta_nestedness_value = beta.pair(betapart_core_object)$beta.sne %>% as.double()
    beta_total_value = beta.pair(betapart_core_object)$beta.sor %>% as.double()
    
    #Gamma diversity: Meta-ecosystem richness
    metaecosystem_richness_value = colSums(species_vector_two_patches) %>%
      specnumber()
    
    #Put everything together
    ds_metaecosystems[[row_n]] = ds_patches %>%
      filter(culture_ID %in% current_IDs,
             time_point == time_point_input) %>%
      summarise(
        total_metaecosystem_bioarea_mm2 = sum(bioarea_tot_mm2),
        total_metaecosystem_Ble_indiv = sum(Ble_tot_indiv),
        total_metaecosystem_Cep_indiv = sum(Cep_tot_indiv),
        total_metaecosystem_Col_indiv = sum(Col_tot_indiv),
        total_metaecosystem_Eug_indiv = sum(Eug_tot_indiv),
        total_metaecosystem_Eup_indiv = sum(Eup_tot_indiv),
        total_metaecosystem_Lox_indiv = sum(Lox_tot_indiv),
        total_metaecosystem_Pau_indiv = sum(Pau_tot_indiv),
        total_metaecosystem_Pca_indiv = sum(Pca_tot_indiv),
        total_metaecosystem_Spi_indiv = sum(Spi_tot_indiv),
        total_metaecosystem_Spi_te_indiv = sum(Spi_te_tot_indiv),
        total_metaecosystem_Tet_indiv = sum(Tet_tot_indiv)
      ) %>%
      mutate(
        system_nr = current_system_nr,
        patches_combined = current_combination,
        metaecosystem_type = current_metaeco_type,
        disturbance = current_disturbance,
        time_point = time_point_input,
        day = current_day,
        jaccard_index = jaccard_index_value,
        bray_curtis = bray_curtis_value,
        beta_spatial_turnover = beta_spatial_turnover_value,
        beta_nestedness = beta_nestedness_value,
        beta_total = beta_total_value,
        metaecosystem_richness = metaecosystem_richness_value,
        mean_shannon = shannon_value,
        mean_richness = mean_richness_value
      ) %>%
      ungroup()
    
  }
}
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): missing
## values in results
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "bray"): missing values
## in results
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): missing
## values in results
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "bray"): missing values
## in results
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
##                  meaningless in method "jaccard"
## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
##                  meaningless in method "bray"
ds_metaecosystems = ds_metaecosystems %>%
  bind_rows() %>%
  as.data.frame() %>%
  select(
    time_point,
    day,
    system_nr,
    patches_combined,
    disturbance,
    metaecosystem_type,
    mean_shannon,
    mean_richness,
    jaccard_index,
    bray_curtis,
    beta_spatial_turnover,
    beta_nestedness,
    beta_total,
    metaecosystem_richness,
    total_metaecosystem_bioarea_mm2,
    paste0("total_metaecosystem_", protist_species, "_indiv")
  )
expect_equal(nrow(ds_metaecosystems), 
             n_time_points * n_patches_combinations)
#Add column connection
ds_metaecosystems$connection <-
  ifelse(
    ds_metaecosystems$metaecosystem_type %in% c("Medium-Medium isolated",
                                                "Small-Large isolated"),
    yes = "isolated",
    no = "connected"
  )
#Add column patch size symmetry
ds_metaecosystems$patch_size_symmetry <-
  ifelse(
    ds_metaecosystems$metaecosystem_type %in% c("Small-Large isolated",
                                                "Small-Large meta-ecosystem"),
    yes = "asymmetric",
    no = "symmetric"
  )
#Reorder the meta-ecosystems
ds_metaecosystems = ds_metaecosystems %>%
  mutate(metaecosystem_type = factor(metaecosystem_type,
                                     levels = metaecosystem_types_ordered))
#Join ...
full_join(ds_metaecosystems,
          isolated_combinations_sets)
## Warning in full_join(ds_metaecosystems, isolated_combinations_sets): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
##      time_point day system_nr patches_combined disturbance   metaecosystem_type
## 1             0   0      1001             1|11         low Small-Large isolated
## 2             0   0      1001             1|11         low Small-Large isolated
## 3             0   0      1001             1|11         low Small-Large isolated
## 4             0   0      1001             1|11         low Small-Large isolated
## 5             0   0      1001             1|11         low Small-Large isolated
## 6             0   0      1001             1|11         low Small-Large isolated
## 7             0   0      1001             1|11         low Small-Large isolated
## 8             0   0      1001             1|11         low Small-Large isolated
## 9             0   0      1001             1|11         low Small-Large isolated
## 10            0   0      1001             1|11         low Small-Large isolated
## 11            0   0      1001             1|11         low Small-Large isolated
## 12            0   0      1001             1|11         low Small-Large isolated
## 13            0   0      1001             1|11         low Small-Large isolated
## 14            0   0      1001             1|11         low Small-Large isolated
## 15            0   0      1001             1|11         low Small-Large isolated
## 16            0   0      1001             1|11         low Small-Large isolated
## 17            0   0      1001             1|11         low Small-Large isolated
## 18            0   0      1001             1|11         low Small-Large isolated
## 19            0   0      1001             1|11         low Small-Large isolated
## 20            0   0      1001             1|11         low Small-Large isolated
## 21            0   0      1001             1|11         low Small-Large isolated
## 22            0   0      1001             1|11         low Small-Large isolated
## 23            0   0      1001             1|11         low Small-Large isolated
## 24            0   0      1001             1|11         low Small-Large isolated
## 25            1   4      1001             1|11         low Small-Large isolated
## 26            1   4      1001             1|11         low Small-Large isolated
## 27            1   4      1001             1|11         low Small-Large isolated
## 28            1   4      1001             1|11         low Small-Large isolated
## 29            1   4      1001             1|11         low Small-Large isolated
## 30            1   4      1001             1|11         low Small-Large isolated
## 31            1   4      1001             1|11         low Small-Large isolated
## 32            1   4      1001             1|11         low Small-Large isolated
## 33            1   4      1001             1|11         low Small-Large isolated
## 34            1   4      1001             1|11         low Small-Large isolated
## 35            1   4      1001             1|11         low Small-Large isolated
## 36            1   4      1001             1|11         low Small-Large isolated
## 37            1   4      1001             1|11         low Small-Large isolated
## 38            1   4      1001             1|11         low Small-Large isolated
## 39            1   4      1001             1|11         low Small-Large isolated
## 40            1   4      1001             1|11         low Small-Large isolated
## 41            1   4      1001             1|11         low Small-Large isolated
## 42            1   4      1001             1|11         low Small-Large isolated
## 43            1   4      1001             1|11         low Small-Large isolated
## 44            1   4      1001             1|11         low Small-Large isolated
## 45            1   4      1001             1|11         low Small-Large isolated
## 46            1   4      1001             1|11         low Small-Large isolated
## 47            1   4      1001             1|11         low Small-Large isolated
## 48            1   4      1001             1|11         low Small-Large isolated
## 49            2   8      1001             1|11         low Small-Large isolated
## 50            2   8      1001             1|11         low Small-Large isolated
## 51            2   8      1001             1|11         low Small-Large isolated
## 52            2   8      1001             1|11         low Small-Large isolated
## 53            2   8      1001             1|11         low Small-Large isolated
## 54            2   8      1001             1|11         low Small-Large isolated
## 55            2   8      1001             1|11         low Small-Large isolated
## 56            2   8      1001             1|11         low Small-Large isolated
## 57            2   8      1001             1|11         low Small-Large isolated
## 58            2   8      1001             1|11         low Small-Large isolated
## 59            2   8      1001             1|11         low Small-Large isolated
## 60            2   8      1001             1|11         low Small-Large isolated
## 61            2   8      1001             1|11         low Small-Large isolated
## 62            2   8      1001             1|11         low Small-Large isolated
## 63            2   8      1001             1|11         low Small-Large isolated
## 64            2   8      1001             1|11         low Small-Large isolated
## 65            2   8      1001             1|11         low Small-Large isolated
## 66            2   8      1001             1|11         low Small-Large isolated
## 67            2   8      1001             1|11         low Small-Large isolated
## 68            2   8      1001             1|11         low Small-Large isolated
## 69            2   8      1001             1|11         low Small-Large isolated
## 70            2   8      1001             1|11         low Small-Large isolated
## 71            2   8      1001             1|11         low Small-Large isolated
## 72            2   8      1001             1|11         low Small-Large isolated
## 73            3  12      1001             1|11         low Small-Large isolated
## 74            3  12      1001             1|11         low Small-Large isolated
## 75            3  12      1001             1|11         low Small-Large isolated
## 76            3  12      1001             1|11         low Small-Large isolated
## 77            3  12      1001             1|11         low Small-Large isolated
## 78            3  12      1001             1|11         low Small-Large isolated
## 79            3  12      1001             1|11         low Small-Large isolated
## 80            3  12      1001             1|11         low Small-Large isolated
## 81            3  12      1001             1|11         low Small-Large isolated
## 82            3  12      1001             1|11         low Small-Large isolated
## 83            3  12      1001             1|11         low Small-Large isolated
## 84            3  12      1001             1|11         low Small-Large isolated
## 85            3  12      1001             1|11         low Small-Large isolated
## 86            3  12      1001             1|11         low Small-Large isolated
## 87            3  12      1001             1|11         low Small-Large isolated
## 88            3  12      1001             1|11         low Small-Large isolated
## 89            3  12      1001             1|11         low Small-Large isolated
## 90            3  12      1001             1|11         low Small-Large isolated
## 91            3  12      1001             1|11         low Small-Large isolated
## 92            3  12      1001             1|11         low Small-Large isolated
## 93            3  12      1001             1|11         low Small-Large isolated
## 94            3  12      1001             1|11         low Small-Large isolated
## 95            3  12      1001             1|11         low Small-Large isolated
## 96            3  12      1001             1|11         low Small-Large isolated
## 97            4  16      1001             1|11         low Small-Large isolated
## 98            4  16      1001             1|11         low Small-Large isolated
## 99            4  16      1001             1|11         low Small-Large isolated
## 100           4  16      1001             1|11         low Small-Large isolated
## 101           4  16      1001             1|11         low Small-Large isolated
## 102           4  16      1001             1|11         low Small-Large isolated
## 103           4  16      1001             1|11         low Small-Large isolated
## 104           4  16      1001             1|11         low Small-Large isolated
## 105           4  16      1001             1|11         low Small-Large isolated
## 106           4  16      1001             1|11         low Small-Large isolated
## 107           4  16      1001             1|11         low Small-Large isolated
## 108           4  16      1001             1|11         low Small-Large isolated
## 109           4  16      1001             1|11         low Small-Large isolated
## 110           4  16      1001             1|11         low Small-Large isolated
## 111           4  16      1001             1|11         low Small-Large isolated
## 112           4  16      1001             1|11         low Small-Large isolated
## 113           4  16      1001             1|11         low Small-Large isolated
## 114           4  16      1001             1|11         low Small-Large isolated
## 115           4  16      1001             1|11         low Small-Large isolated
## 116           4  16      1001             1|11         low Small-Large isolated
## 117           4  16      1001             1|11         low Small-Large isolated
## 118           4  16      1001             1|11         low Small-Large isolated
## 119           4  16      1001             1|11         low Small-Large isolated
## 120           4  16      1001             1|11         low Small-Large isolated
## 121           5  20      1001             1|11         low Small-Large isolated
## 122           5  20      1001             1|11         low Small-Large isolated
## 123           5  20      1001             1|11         low Small-Large isolated
## 124           5  20      1001             1|11         low Small-Large isolated
## 125           5  20      1001             1|11         low Small-Large isolated
## 126           5  20      1001             1|11         low Small-Large isolated
## 127           5  20      1001             1|11         low Small-Large isolated
## 128           5  20      1001             1|11         low Small-Large isolated
## 129           5  20      1001             1|11         low Small-Large isolated
## 130           5  20      1001             1|11         low Small-Large isolated
## 131           5  20      1001             1|11         low Small-Large isolated
## 132           5  20      1001             1|11         low Small-Large isolated
## 133           5  20      1001             1|11         low Small-Large isolated
## 134           5  20      1001             1|11         low Small-Large isolated
## 135           5  20      1001             1|11         low Small-Large isolated
## 136           5  20      1001             1|11         low Small-Large isolated
## 137           5  20      1001             1|11         low Small-Large isolated
## 138           5  20      1001             1|11         low Small-Large isolated
## 139           5  20      1001             1|11         low Small-Large isolated
## 140           5  20      1001             1|11         low Small-Large isolated
## 141           5  20      1001             1|11         low Small-Large isolated
## 142           5  20      1001             1|11         low Small-Large isolated
## 143           5  20      1001             1|11         low Small-Large isolated
## 144           5  20      1001             1|11         low Small-Large isolated
## 145           6  24      1001             1|11         low Small-Large isolated
## 146           6  24      1001             1|11         low Small-Large isolated
## 147           6  24      1001             1|11         low Small-Large isolated
## 148           6  24      1001             1|11         low Small-Large isolated
## 149           6  24      1001             1|11         low Small-Large isolated
## 150           6  24      1001             1|11         low Small-Large isolated
## 151           6  24      1001             1|11         low Small-Large isolated
## 152           6  24      1001             1|11         low Small-Large isolated
## 153           6  24      1001             1|11         low Small-Large isolated
## 154           6  24      1001             1|11         low Small-Large isolated
## 155           6  24      1001             1|11         low Small-Large isolated
## 156           6  24      1001             1|11         low Small-Large isolated
## 157           6  24      1001             1|11         low Small-Large isolated
## 158           6  24      1001             1|11         low Small-Large isolated
## 159           6  24      1001             1|11         low Small-Large isolated
## 160           6  24      1001             1|11         low Small-Large isolated
## 161           6  24      1001             1|11         low Small-Large isolated
## 162           6  24      1001             1|11         low Small-Large isolated
## 163           6  24      1001             1|11         low Small-Large isolated
## 164           6  24      1001             1|11         low Small-Large isolated
## 165           6  24      1001             1|11         low Small-Large isolated
## 166           6  24      1001             1|11         low Small-Large isolated
## 167           6  24      1001             1|11         low Small-Large isolated
## 168           6  24      1001             1|11         low Small-Large isolated
## 169           7  28      1001             1|11         low Small-Large isolated
## 170           7  28      1001             1|11         low Small-Large isolated
## 171           7  28      1001             1|11         low Small-Large isolated
## 172           7  28      1001             1|11         low Small-Large isolated
## 173           7  28      1001             1|11         low Small-Large isolated
## 174           7  28      1001             1|11         low Small-Large isolated
## 175           7  28      1001             1|11         low Small-Large isolated
## 176           7  28      1001             1|11         low Small-Large isolated
## 177           7  28      1001             1|11         low Small-Large isolated
## 178           7  28      1001             1|11         low Small-Large isolated
## 179           7  28      1001             1|11         low Small-Large isolated
## 180           7  28      1001             1|11         low Small-Large isolated
## 181           7  28      1001             1|11         low Small-Large isolated
## 182           7  28      1001             1|11         low Small-Large isolated
## 183           7  28      1001             1|11         low Small-Large isolated
## 184           7  28      1001             1|11         low Small-Large isolated
## 185           7  28      1001             1|11         low Small-Large isolated
## 186           7  28      1001             1|11         low Small-Large isolated
## 187           7  28      1001             1|11         low Small-Large isolated
## 188           7  28      1001             1|11         low Small-Large isolated
## 189           7  28      1001             1|11         low Small-Large isolated
## 190           7  28      1001             1|11         low Small-Large isolated
## 191           7  28      1001             1|11         low Small-Large isolated
## 192           7  28      1001             1|11         low Small-Large isolated
## 193           0   0      1002             1|12         low Small-Large isolated
## 194           0   0      1002             1|12         low Small-Large isolated
## 195           0   0      1002             1|12         low Small-Large isolated
## 196           0   0      1002             1|12         low Small-Large isolated
## 197           0   0      1002             1|12         low Small-Large isolated
## 198           0   0      1002             1|12         low Small-Large isolated
## 199           0   0      1002             1|12         low Small-Large isolated
## 200           0   0      1002             1|12         low Small-Large isolated
## 201           0   0      1002             1|12         low Small-Large isolated
## 202           0   0      1002             1|12         low Small-Large isolated
## 203           0   0      1002             1|12         low Small-Large isolated
## 204           0   0      1002             1|12         low Small-Large isolated
## 205           0   0      1002             1|12         low Small-Large isolated
## 206           0   0      1002             1|12         low Small-Large isolated
## 207           0   0      1002             1|12         low Small-Large isolated
## 208           0   0      1002             1|12         low Small-Large isolated
## 209           0   0      1002             1|12         low Small-Large isolated
## 210           0   0      1002             1|12         low Small-Large isolated
## 211           0   0      1002             1|12         low Small-Large isolated
## 212           0   0      1002             1|12         low Small-Large isolated
## 213           0   0      1002             1|12         low Small-Large isolated
## 214           0   0      1002             1|12         low Small-Large isolated
## 215           0   0      1002             1|12         low Small-Large isolated
## 216           0   0      1002             1|12         low Small-Large isolated
## 217           1   4      1002             1|12         low Small-Large isolated
## 218           1   4      1002             1|12         low Small-Large isolated
## 219           1   4      1002             1|12         low Small-Large isolated
## 220           1   4      1002             1|12         low Small-Large isolated
## 221           1   4      1002             1|12         low Small-Large isolated
## 222           1   4      1002             1|12         low Small-Large isolated
## 223           1   4      1002             1|12         low Small-Large isolated
## 224           1   4      1002             1|12         low Small-Large isolated
## 225           1   4      1002             1|12         low Small-Large isolated
## 226           1   4      1002             1|12         low Small-Large isolated
## 227           1   4      1002             1|12         low Small-Large isolated
## 228           1   4      1002             1|12         low Small-Large isolated
## 229           1   4      1002             1|12         low Small-Large isolated
## 230           1   4      1002             1|12         low Small-Large isolated
## 231           1   4      1002             1|12         low Small-Large isolated
## 232           1   4      1002             1|12         low Small-Large isolated
## 233           1   4      1002             1|12         low Small-Large isolated
## 234           1   4      1002             1|12         low Small-Large isolated
## 235           1   4      1002             1|12         low Small-Large isolated
## 236           1   4      1002             1|12         low Small-Large isolated
## 237           1   4      1002             1|12         low Small-Large isolated
## 238           1   4      1002             1|12         low Small-Large isolated
## 239           1   4      1002             1|12         low Small-Large isolated
## 240           1   4      1002             1|12         low Small-Large isolated
## 241           2   8      1002             1|12         low Small-Large isolated
## 242           2   8      1002             1|12         low Small-Large isolated
## 243           2   8      1002             1|12         low Small-Large isolated
## 244           2   8      1002             1|12         low Small-Large isolated
## 245           2   8      1002             1|12         low Small-Large isolated
## 246           2   8      1002             1|12         low Small-Large isolated
## 247           2   8      1002             1|12         low Small-Large isolated
## 248           2   8      1002             1|12         low Small-Large isolated
## 249           2   8      1002             1|12         low Small-Large isolated
## 250           2   8      1002             1|12         low Small-Large isolated
## 251           2   8      1002             1|12         low Small-Large isolated
## 252           2   8      1002             1|12         low Small-Large isolated
## 253           2   8      1002             1|12         low Small-Large isolated
## 254           2   8      1002             1|12         low Small-Large isolated
## 255           2   8      1002             1|12         low Small-Large isolated
## 256           2   8      1002             1|12         low Small-Large isolated
## 257           2   8      1002             1|12         low Small-Large isolated
## 258           2   8      1002             1|12         low Small-Large isolated
## 259           2   8      1002             1|12         low Small-Large isolated
## 260           2   8      1002             1|12         low Small-Large isolated
## 261           2   8      1002             1|12         low Small-Large isolated
## 262           2   8      1002             1|12         low Small-Large isolated
## 263           2   8      1002             1|12         low Small-Large isolated
## 264           2   8      1002             1|12         low Small-Large isolated
## 265           3  12      1002             1|12         low Small-Large isolated
## 266           3  12      1002             1|12         low Small-Large isolated
## 267           3  12      1002             1|12         low Small-Large isolated
## 268           3  12      1002             1|12         low Small-Large isolated
## 269           3  12      1002             1|12         low Small-Large isolated
## 270           3  12      1002             1|12         low Small-Large isolated
## 271           3  12      1002             1|12         low Small-Large isolated
## 272           3  12      1002             1|12         low Small-Large isolated
## 273           3  12      1002             1|12         low Small-Large isolated
## 274           3  12      1002             1|12         low Small-Large isolated
## 275           3  12      1002             1|12         low Small-Large isolated
## 276           3  12      1002             1|12         low Small-Large isolated
## 277           3  12      1002             1|12         low Small-Large isolated
## 278           3  12      1002             1|12         low Small-Large isolated
## 279           3  12      1002             1|12         low Small-Large isolated
## 280           3  12      1002             1|12         low Small-Large isolated
## 281           3  12      1002             1|12         low Small-Large isolated
## 282           3  12      1002             1|12         low Small-Large isolated
## 283           3  12      1002             1|12         low Small-Large isolated
## 284           3  12      1002             1|12         low Small-Large isolated
## 285           3  12      1002             1|12         low Small-Large isolated
## 286           3  12      1002             1|12         low Small-Large isolated
## 287           3  12      1002             1|12         low Small-Large isolated
## 288           3  12      1002             1|12         low Small-Large isolated
## 289           4  16      1002             1|12         low Small-Large isolated
## 290           4  16      1002             1|12         low Small-Large isolated
## 291           4  16      1002             1|12         low Small-Large isolated
## 292           4  16      1002             1|12         low Small-Large isolated
## 293           4  16      1002             1|12         low Small-Large isolated
## 294           4  16      1002             1|12         low Small-Large isolated
## 295           4  16      1002             1|12         low Small-Large isolated
## 296           4  16      1002             1|12         low Small-Large isolated
## 297           4  16      1002             1|12         low Small-Large isolated
## 298           4  16      1002             1|12         low Small-Large isolated
## 299           4  16      1002             1|12         low Small-Large isolated
## 300           4  16      1002             1|12         low Small-Large isolated
## 301           4  16      1002             1|12         low Small-Large isolated
## 302           4  16      1002             1|12         low Small-Large isolated
## 303           4  16      1002             1|12         low Small-Large isolated
## 304           4  16      1002             1|12         low Small-Large isolated
## 305           4  16      1002             1|12         low Small-Large isolated
## 306           4  16      1002             1|12         low Small-Large isolated
## 307           4  16      1002             1|12         low Small-Large isolated
## 308           4  16      1002             1|12         low Small-Large isolated
## 309           4  16      1002             1|12         low Small-Large isolated
## 310           4  16      1002             1|12         low Small-Large isolated
## 311           4  16      1002             1|12         low Small-Large isolated
## 312           4  16      1002             1|12         low Small-Large isolated
## 313           5  20      1002             1|12         low Small-Large isolated
## 314           5  20      1002             1|12         low Small-Large isolated
## 315           5  20      1002             1|12         low Small-Large isolated
## 316           5  20      1002             1|12         low Small-Large isolated
## 317           5  20      1002             1|12         low Small-Large isolated
## 318           5  20      1002             1|12         low Small-Large isolated
## 319           5  20      1002             1|12         low Small-Large isolated
## 320           5  20      1002             1|12         low Small-Large isolated
## 321           5  20      1002             1|12         low Small-Large isolated
## 322           5  20      1002             1|12         low Small-Large isolated
## 323           5  20      1002             1|12         low Small-Large isolated
## 324           5  20      1002             1|12         low Small-Large isolated
## 325           5  20      1002             1|12         low Small-Large isolated
## 326           5  20      1002             1|12         low Small-Large isolated
## 327           5  20      1002             1|12         low Small-Large isolated
## 328           5  20      1002             1|12         low Small-Large isolated
## 329           5  20      1002             1|12         low Small-Large isolated
## 330           5  20      1002             1|12         low Small-Large isolated
## 331           5  20      1002             1|12         low Small-Large isolated
## 332           5  20      1002             1|12         low Small-Large isolated
## 333           5  20      1002             1|12         low Small-Large isolated
## 334           5  20      1002             1|12         low Small-Large isolated
## 335           5  20      1002             1|12         low Small-Large isolated
## 336           5  20      1002             1|12         low Small-Large isolated
## 337           6  24      1002             1|12         low Small-Large isolated
## 338           6  24      1002             1|12         low Small-Large isolated
## 339           6  24      1002             1|12         low Small-Large isolated
## 340           6  24      1002             1|12         low Small-Large isolated
## 341           6  24      1002             1|12         low Small-Large isolated
## 342           6  24      1002             1|12         low Small-Large isolated
## 343           6  24      1002             1|12         low Small-Large isolated
## 344           6  24      1002             1|12         low Small-Large isolated
## 345           6  24      1002             1|12         low Small-Large isolated
## 346           6  24      1002             1|12         low Small-Large isolated
## 347           6  24      1002             1|12         low Small-Large isolated
## 348           6  24      1002             1|12         low Small-Large isolated
## 349           6  24      1002             1|12         low Small-Large isolated
## 350           6  24      1002             1|12         low Small-Large isolated
## 351           6  24      1002             1|12         low Small-Large isolated
## 352           6  24      1002             1|12         low Small-Large isolated
## 353           6  24      1002             1|12         low Small-Large isolated
## 354           6  24      1002             1|12         low Small-Large isolated
## 355           6  24      1002             1|12         low Small-Large isolated
## 356           6  24      1002             1|12         low Small-Large isolated
## 357           6  24      1002             1|12         low Small-Large isolated
## 358           6  24      1002             1|12         low Small-Large isolated
## 359           6  24      1002             1|12         low Small-Large isolated
## 360           6  24      1002             1|12         low Small-Large isolated
## 361           7  28      1002             1|12         low Small-Large isolated
## 362           7  28      1002             1|12         low Small-Large isolated
## 363           7  28      1002             1|12         low Small-Large isolated
## 364           7  28      1002             1|12         low Small-Large isolated
## 365           7  28      1002             1|12         low Small-Large isolated
## 366           7  28      1002             1|12         low Small-Large isolated
## 367           7  28      1002             1|12         low Small-Large isolated
## 368           7  28      1002             1|12         low Small-Large isolated
## 369           7  28      1002             1|12         low Small-Large isolated
## 370           7  28      1002             1|12         low Small-Large isolated
## 371           7  28      1002             1|12         low Small-Large isolated
## 372           7  28      1002             1|12         low Small-Large isolated
## 373           7  28      1002             1|12         low Small-Large isolated
## 374           7  28      1002             1|12         low Small-Large isolated
## 375           7  28      1002             1|12         low Small-Large isolated
## 376           7  28      1002             1|12         low Small-Large isolated
## 377           7  28      1002             1|12         low Small-Large isolated
## 378           7  28      1002             1|12         low Small-Large isolated
## 379           7  28      1002             1|12         low Small-Large isolated
## 380           7  28      1002             1|12         low Small-Large isolated
## 381           7  28      1002             1|12         low Small-Large isolated
## 382           7  28      1002             1|12         low Small-Large isolated
## 383           7  28      1002             1|12         low Small-Large isolated
## 384           7  28      1002             1|12         low Small-Large isolated
## 385           0   0      1003             1|13         low Small-Large isolated
## 386           0   0      1003             1|13         low Small-Large isolated
## 387           0   0      1003             1|13         low Small-Large isolated
## 388           0   0      1003             1|13         low Small-Large isolated
## 389           0   0      1003             1|13         low Small-Large isolated
## 390           0   0      1003             1|13         low Small-Large isolated
## 391           0   0      1003             1|13         low Small-Large isolated
## 392           0   0      1003             1|13         low Small-Large isolated
## 393           0   0      1003             1|13         low Small-Large isolated
## 394           0   0      1003             1|13         low Small-Large isolated
## 395           0   0      1003             1|13         low Small-Large isolated
## 396           0   0      1003             1|13         low Small-Large isolated
## 397           0   0      1003             1|13         low Small-Large isolated
## 398           0   0      1003             1|13         low Small-Large isolated
## 399           0   0      1003             1|13         low Small-Large isolated
## 400           0   0      1003             1|13         low Small-Large isolated
## 401           0   0      1003             1|13         low Small-Large isolated
## 402           0   0      1003             1|13         low Small-Large isolated
## 403           0   0      1003             1|13         low Small-Large isolated
## 404           0   0      1003             1|13         low Small-Large isolated
## 405           0   0      1003             1|13         low Small-Large isolated
## 406           0   0      1003             1|13         low Small-Large isolated
## 407           0   0      1003             1|13         low Small-Large isolated
## 408           0   0      1003             1|13         low Small-Large isolated
## 409           1   4      1003             1|13         low Small-Large isolated
## 410           1   4      1003             1|13         low Small-Large isolated
## 411           1   4      1003             1|13         low Small-Large isolated
## 412           1   4      1003             1|13         low Small-Large isolated
## 413           1   4      1003             1|13         low Small-Large isolated
## 414           1   4      1003             1|13         low Small-Large isolated
## 415           1   4      1003             1|13         low Small-Large isolated
## 416           1   4      1003             1|13         low Small-Large isolated
## 417           1   4      1003             1|13         low Small-Large isolated
## 418           1   4      1003             1|13         low Small-Large isolated
## 419           1   4      1003             1|13         low Small-Large isolated
## 420           1   4      1003             1|13         low Small-Large isolated
## 421           1   4      1003             1|13         low Small-Large isolated
## 422           1   4      1003             1|13         low Small-Large isolated
## 423           1   4      1003             1|13         low Small-Large isolated
## 424           1   4      1003             1|13         low Small-Large isolated
## 425           1   4      1003             1|13         low Small-Large isolated
## 426           1   4      1003             1|13         low Small-Large isolated
## 427           1   4      1003             1|13         low Small-Large isolated
## 428           1   4      1003             1|13         low Small-Large isolated
## 429           1   4      1003             1|13         low Small-Large isolated
## 430           1   4      1003             1|13         low Small-Large isolated
## 431           1   4      1003             1|13         low Small-Large isolated
## 432           1   4      1003             1|13         low Small-Large isolated
## 433           2   8      1003             1|13         low Small-Large isolated
## 434           2   8      1003             1|13         low Small-Large isolated
## 435           2   8      1003             1|13         low Small-Large isolated
## 436           2   8      1003             1|13         low Small-Large isolated
## 437           2   8      1003             1|13         low Small-Large isolated
## 438           2   8      1003             1|13         low Small-Large isolated
## 439           2   8      1003             1|13         low Small-Large isolated
## 440           2   8      1003             1|13         low Small-Large isolated
## 441           2   8      1003             1|13         low Small-Large isolated
## 442           2   8      1003             1|13         low Small-Large isolated
## 443           2   8      1003             1|13         low Small-Large isolated
## 444           2   8      1003             1|13         low Small-Large isolated
## 445           2   8      1003             1|13         low Small-Large isolated
## 446           2   8      1003             1|13         low Small-Large isolated
## 447           2   8      1003             1|13         low Small-Large isolated
## 448           2   8      1003             1|13         low Small-Large isolated
## 449           2   8      1003             1|13         low Small-Large isolated
## 450           2   8      1003             1|13         low Small-Large isolated
## 451           2   8      1003             1|13         low Small-Large isolated
## 452           2   8      1003             1|13         low Small-Large isolated
## 453           2   8      1003             1|13         low Small-Large isolated
## 454           2   8      1003             1|13         low Small-Large isolated
## 455           2   8      1003             1|13         low Small-Large isolated
## 456           2   8      1003             1|13         low Small-Large isolated
## 457           3  12      1003             1|13         low Small-Large isolated
## 458           3  12      1003             1|13         low Small-Large isolated
## 459           3  12      1003             1|13         low Small-Large isolated
## 460           3  12      1003             1|13         low Small-Large isolated
## 461           3  12      1003             1|13         low Small-Large isolated
## 462           3  12      1003             1|13         low Small-Large isolated
## 463           3  12      1003             1|13         low Small-Large isolated
## 464           3  12      1003             1|13         low Small-Large isolated
## 465           3  12      1003             1|13         low Small-Large isolated
## 466           3  12      1003             1|13         low Small-Large isolated
## 467           3  12      1003             1|13         low Small-Large isolated
## 468           3  12      1003             1|13         low Small-Large isolated
## 469           3  12      1003             1|13         low Small-Large isolated
## 470           3  12      1003             1|13         low Small-Large isolated
## 471           3  12      1003             1|13         low Small-Large isolated
## 472           3  12      1003             1|13         low Small-Large isolated
## 473           3  12      1003             1|13         low Small-Large isolated
## 474           3  12      1003             1|13         low Small-Large isolated
## 475           3  12      1003             1|13         low Small-Large isolated
## 476           3  12      1003             1|13         low Small-Large isolated
## 477           3  12      1003             1|13         low Small-Large isolated
## 478           3  12      1003             1|13         low Small-Large isolated
## 479           3  12      1003             1|13         low Small-Large isolated
## 480           3  12      1003             1|13         low Small-Large isolated
## 481           4  16      1003             1|13         low Small-Large isolated
## 482           4  16      1003             1|13         low Small-Large isolated
## 483           4  16      1003             1|13         low Small-Large isolated
## 484           4  16      1003             1|13         low Small-Large isolated
## 485           4  16      1003             1|13         low Small-Large isolated
## 486           4  16      1003             1|13         low Small-Large isolated
## 487           4  16      1003             1|13         low Small-Large isolated
## 488           4  16      1003             1|13         low Small-Large isolated
## 489           4  16      1003             1|13         low Small-Large isolated
## 490           4  16      1003             1|13         low Small-Large isolated
## 491           4  16      1003             1|13         low Small-Large isolated
## 492           4  16      1003             1|13         low Small-Large isolated
## 493           4  16      1003             1|13         low Small-Large isolated
## 494           4  16      1003             1|13         low Small-Large isolated
## 495           4  16      1003             1|13         low Small-Large isolated
## 496           4  16      1003             1|13         low Small-Large isolated
## 497           4  16      1003             1|13         low Small-Large isolated
## 498           4  16      1003             1|13         low Small-Large isolated
## 499           4  16      1003             1|13         low Small-Large isolated
## 500           4  16      1003             1|13         low Small-Large isolated
## 501           4  16      1003             1|13         low Small-Large isolated
## 502           4  16      1003             1|13         low Small-Large isolated
## 503           4  16      1003             1|13         low Small-Large isolated
## 504           4  16      1003             1|13         low Small-Large isolated
## 505           5  20      1003             1|13         low Small-Large isolated
## 506           5  20      1003             1|13         low Small-Large isolated
## 507           5  20      1003             1|13         low Small-Large isolated
## 508           5  20      1003             1|13         low Small-Large isolated
## 509           5  20      1003             1|13         low Small-Large isolated
## 510           5  20      1003             1|13         low Small-Large isolated
## 511           5  20      1003             1|13         low Small-Large isolated
## 512           5  20      1003             1|13         low Small-Large isolated
## 513           5  20      1003             1|13         low Small-Large isolated
## 514           5  20      1003             1|13         low Small-Large isolated
## 515           5  20      1003             1|13         low Small-Large isolated
## 516           5  20      1003             1|13         low Small-Large isolated
## 517           5  20      1003             1|13         low Small-Large isolated
## 518           5  20      1003             1|13         low Small-Large isolated
## 519           5  20      1003             1|13         low Small-Large isolated
## 520           5  20      1003             1|13         low Small-Large isolated
## 521           5  20      1003             1|13         low Small-Large isolated
## 522           5  20      1003             1|13         low Small-Large isolated
## 523           5  20      1003             1|13         low Small-Large isolated
## 524           5  20      1003             1|13         low Small-Large isolated
## 525           5  20      1003             1|13         low Small-Large isolated
## 526           5  20      1003             1|13         low Small-Large isolated
## 527           5  20      1003             1|13         low Small-Large isolated
## 528           5  20      1003             1|13         low Small-Large isolated
## 529           6  24      1003             1|13         low Small-Large isolated
## 530           6  24      1003             1|13         low Small-Large isolated
## 531           6  24      1003             1|13         low Small-Large isolated
## 532           6  24      1003             1|13         low Small-Large isolated
## 533           6  24      1003             1|13         low Small-Large isolated
## 534           6  24      1003             1|13         low Small-Large isolated
## 535           6  24      1003             1|13         low Small-Large isolated
## 536           6  24      1003             1|13         low Small-Large isolated
## 537           6  24      1003             1|13         low Small-Large isolated
## 538           6  24      1003             1|13         low Small-Large isolated
## 539           6  24      1003             1|13         low Small-Large isolated
## 540           6  24      1003             1|13         low Small-Large isolated
## 541           6  24      1003             1|13         low Small-Large isolated
## 542           6  24      1003             1|13         low Small-Large isolated
## 543           6  24      1003             1|13         low Small-Large isolated
## 544           6  24      1003             1|13         low Small-Large isolated
## 545           6  24      1003             1|13         low Small-Large isolated
## 546           6  24      1003             1|13         low Small-Large isolated
## 547           6  24      1003             1|13         low Small-Large isolated
## 548           6  24      1003             1|13         low Small-Large isolated
## 549           6  24      1003             1|13         low Small-Large isolated
## 550           6  24      1003             1|13         low Small-Large isolated
## 551           6  24      1003             1|13         low Small-Large isolated
## 552           6  24      1003             1|13         low Small-Large isolated
## 553           7  28      1003             1|13         low Small-Large isolated
## 554           7  28      1003             1|13         low Small-Large isolated
## 555           7  28      1003             1|13         low Small-Large isolated
## 556           7  28      1003             1|13         low Small-Large isolated
## 557           7  28      1003             1|13         low Small-Large isolated
## 558           7  28      1003             1|13         low Small-Large isolated
## 559           7  28      1003             1|13         low Small-Large isolated
## 560           7  28      1003             1|13         low Small-Large isolated
## 561           7  28      1003             1|13         low Small-Large isolated
## 562           7  28      1003             1|13         low Small-Large isolated
## 563           7  28      1003             1|13         low Small-Large isolated
## 564           7  28      1003             1|13         low Small-Large isolated
## 565           7  28      1003             1|13         low Small-Large isolated
## 566           7  28      1003             1|13         low Small-Large isolated
## 567           7  28      1003             1|13         low Small-Large isolated
## 568           7  28      1003             1|13         low Small-Large isolated
## 569           7  28      1003             1|13         low Small-Large isolated
## 570           7  28      1003             1|13         low Small-Large isolated
## 571           7  28      1003             1|13         low Small-Large isolated
## 572           7  28      1003             1|13         low Small-Large isolated
## 573           7  28      1003             1|13         low Small-Large isolated
## 574           7  28      1003             1|13         low Small-Large isolated
## 575           7  28      1003             1|13         low Small-Large isolated
## 576           7  28      1003             1|13         low Small-Large isolated
## 577           0   0      1004             1|14         low Small-Large isolated
## 578           0   0      1004             1|14         low Small-Large isolated
## 579           0   0      1004             1|14         low Small-Large isolated
## 580           0   0      1004             1|14         low Small-Large isolated
## 581           0   0      1004             1|14         low Small-Large isolated
## 582           0   0      1004             1|14         low Small-Large isolated
## 583           0   0      1004             1|14         low Small-Large isolated
## 584           0   0      1004             1|14         low Small-Large isolated
## 585           0   0      1004             1|14         low Small-Large isolated
## 586           0   0      1004             1|14         low Small-Large isolated
## 587           0   0      1004             1|14         low Small-Large isolated
## 588           0   0      1004             1|14         low Small-Large isolated
## 589           0   0      1004             1|14         low Small-Large isolated
## 590           0   0      1004             1|14         low Small-Large isolated
## 591           0   0      1004             1|14         low Small-Large isolated
## 592           0   0      1004             1|14         low Small-Large isolated
## 593           0   0      1004             1|14         low Small-Large isolated
## 594           0   0      1004             1|14         low Small-Large isolated
## 595           0   0      1004             1|14         low Small-Large isolated
## 596           0   0      1004             1|14         low Small-Large isolated
## 597           0   0      1004             1|14         low Small-Large isolated
## 598           0   0      1004             1|14         low Small-Large isolated
## 599           0   0      1004             1|14         low Small-Large isolated
## 600           0   0      1004             1|14         low Small-Large isolated
## 601           1   4      1004             1|14         low Small-Large isolated
## 602           1   4      1004             1|14         low Small-Large isolated
## 603           1   4      1004             1|14         low Small-Large isolated
## 604           1   4      1004             1|14         low Small-Large isolated
## 605           1   4      1004             1|14         low Small-Large isolated
## 606           1   4      1004             1|14         low Small-Large isolated
## 607           1   4      1004             1|14         low Small-Large isolated
## 608           1   4      1004             1|14         low Small-Large isolated
## 609           1   4      1004             1|14         low Small-Large isolated
## 610           1   4      1004             1|14         low Small-Large isolated
## 611           1   4      1004             1|14         low Small-Large isolated
## 612           1   4      1004             1|14         low Small-Large isolated
## 613           1   4      1004             1|14         low Small-Large isolated
## 614           1   4      1004             1|14         low Small-Large isolated
## 615           1   4      1004             1|14         low Small-Large isolated
## 616           1   4      1004             1|14         low Small-Large isolated
## 617           1   4      1004             1|14         low Small-Large isolated
## 618           1   4      1004             1|14         low Small-Large isolated
## 619           1   4      1004             1|14         low Small-Large isolated
## 620           1   4      1004             1|14         low Small-Large isolated
## 621           1   4      1004             1|14         low Small-Large isolated
## 622           1   4      1004             1|14         low Small-Large isolated
## 623           1   4      1004             1|14         low Small-Large isolated
## 624           1   4      1004             1|14         low Small-Large isolated
## 625           2   8      1004             1|14         low Small-Large isolated
## 626           2   8      1004             1|14         low Small-Large isolated
## 627           2   8      1004             1|14         low Small-Large isolated
## 628           2   8      1004             1|14         low Small-Large isolated
## 629           2   8      1004             1|14         low Small-Large isolated
## 630           2   8      1004             1|14         low Small-Large isolated
## 631           2   8      1004             1|14         low Small-Large isolated
## 632           2   8      1004             1|14         low Small-Large isolated
## 633           2   8      1004             1|14         low Small-Large isolated
## 634           2   8      1004             1|14         low Small-Large isolated
## 635           2   8      1004             1|14         low Small-Large isolated
## 636           2   8      1004             1|14         low Small-Large isolated
## 637           2   8      1004             1|14         low Small-Large isolated
## 638           2   8      1004             1|14         low Small-Large isolated
## 639           2   8      1004             1|14         low Small-Large isolated
## 640           2   8      1004             1|14         low Small-Large isolated
## 641           2   8      1004             1|14         low Small-Large isolated
## 642           2   8      1004             1|14         low Small-Large isolated
## 643           2   8      1004             1|14         low Small-Large isolated
## 644           2   8      1004             1|14         low Small-Large isolated
## 645           2   8      1004             1|14         low Small-Large isolated
## 646           2   8      1004             1|14         low Small-Large isolated
## 647           2   8      1004             1|14         low Small-Large isolated
## 648           2   8      1004             1|14         low Small-Large isolated
## 649           3  12      1004             1|14         low Small-Large isolated
## 650           3  12      1004             1|14         low Small-Large isolated
## 651           3  12      1004             1|14         low Small-Large isolated
## 652           3  12      1004             1|14         low Small-Large isolated
## 653           3  12      1004             1|14         low Small-Large isolated
## 654           3  12      1004             1|14         low Small-Large isolated
## 655           3  12      1004             1|14         low Small-Large isolated
## 656           3  12      1004             1|14         low Small-Large isolated
## 657           3  12      1004             1|14         low Small-Large isolated
## 658           3  12      1004             1|14         low Small-Large isolated
## 659           3  12      1004             1|14         low Small-Large isolated
## 660           3  12      1004             1|14         low Small-Large isolated
## 661           3  12      1004             1|14         low Small-Large isolated
## 662           3  12      1004             1|14         low Small-Large isolated
## 663           3  12      1004             1|14         low Small-Large isolated
## 664           3  12      1004             1|14         low Small-Large isolated
## 665           3  12      1004             1|14         low Small-Large isolated
## 666           3  12      1004             1|14         low Small-Large isolated
## 667           3  12      1004             1|14         low Small-Large isolated
## 668           3  12      1004             1|14         low Small-Large isolated
## 669           3  12      1004             1|14         low Small-Large isolated
## 670           3  12      1004             1|14         low Small-Large isolated
## 671           3  12      1004             1|14         low Small-Large isolated
## 672           3  12      1004             1|14         low Small-Large isolated
## 673           4  16      1004             1|14         low Small-Large isolated
## 674           4  16      1004             1|14         low Small-Large isolated
## 675           4  16      1004             1|14         low Small-Large isolated
## 676           4  16      1004             1|14         low Small-Large isolated
## 677           4  16      1004             1|14         low Small-Large isolated
## 678           4  16      1004             1|14         low Small-Large isolated
## 679           4  16      1004             1|14         low Small-Large isolated
## 680           4  16      1004             1|14         low Small-Large isolated
## 681           4  16      1004             1|14         low Small-Large isolated
## 682           4  16      1004             1|14         low Small-Large isolated
## 683           4  16      1004             1|14         low Small-Large isolated
## 684           4  16      1004             1|14         low Small-Large isolated
## 685           4  16      1004             1|14         low Small-Large isolated
## 686           4  16      1004             1|14         low Small-Large isolated
## 687           4  16      1004             1|14         low Small-Large isolated
## 688           4  16      1004             1|14         low Small-Large isolated
## 689           4  16      1004             1|14         low Small-Large isolated
## 690           4  16      1004             1|14         low Small-Large isolated
## 691           4  16      1004             1|14         low Small-Large isolated
## 692           4  16      1004             1|14         low Small-Large isolated
## 693           4  16      1004             1|14         low Small-Large isolated
## 694           4  16      1004             1|14         low Small-Large isolated
## 695           4  16      1004             1|14         low Small-Large isolated
## 696           4  16      1004             1|14         low Small-Large isolated
## 697           5  20      1004             1|14         low Small-Large isolated
## 698           5  20      1004             1|14         low Small-Large isolated
## 699           5  20      1004             1|14         low Small-Large isolated
## 700           5  20      1004             1|14         low Small-Large isolated
## 701           5  20      1004             1|14         low Small-Large isolated
## 702           5  20      1004             1|14         low Small-Large isolated
## 703           5  20      1004             1|14         low Small-Large isolated
## 704           5  20      1004             1|14         low Small-Large isolated
## 705           5  20      1004             1|14         low Small-Large isolated
## 706           5  20      1004             1|14         low Small-Large isolated
## 707           5  20      1004             1|14         low Small-Large isolated
## 708           5  20      1004             1|14         low Small-Large isolated
## 709           5  20      1004             1|14         low Small-Large isolated
## 710           5  20      1004             1|14         low Small-Large isolated
## 711           5  20      1004             1|14         low Small-Large isolated
## 712           5  20      1004             1|14         low Small-Large isolated
## 713           5  20      1004             1|14         low Small-Large isolated
## 714           5  20      1004             1|14         low Small-Large isolated
## 715           5  20      1004             1|14         low Small-Large isolated
## 716           5  20      1004             1|14         low Small-Large isolated
## 717           5  20      1004             1|14         low Small-Large isolated
## 718           5  20      1004             1|14         low Small-Large isolated
## 719           5  20      1004             1|14         low Small-Large isolated
## 720           5  20      1004             1|14         low Small-Large isolated
## 721           6  24      1004             1|14         low Small-Large isolated
## 722           6  24      1004             1|14         low Small-Large isolated
## 723           6  24      1004             1|14         low Small-Large isolated
## 724           6  24      1004             1|14         low Small-Large isolated
## 725           6  24      1004             1|14         low Small-Large isolated
## 726           6  24      1004             1|14         low Small-Large isolated
## 727           6  24      1004             1|14         low Small-Large isolated
## 728           6  24      1004             1|14         low Small-Large isolated
## 729           6  24      1004             1|14         low Small-Large isolated
## 730           6  24      1004             1|14         low Small-Large isolated
## 731           6  24      1004             1|14         low Small-Large isolated
## 732           6  24      1004             1|14         low Small-Large isolated
## 733           6  24      1004             1|14         low Small-Large isolated
## 734           6  24      1004             1|14         low Small-Large isolated
## 735           6  24      1004             1|14         low Small-Large isolated
## 736           6  24      1004             1|14         low Small-Large isolated
## 737           6  24      1004             1|14         low Small-Large isolated
## 738           6  24      1004             1|14         low Small-Large isolated
## 739           6  24      1004             1|14         low Small-Large isolated
## 740           6  24      1004             1|14         low Small-Large isolated
## 741           6  24      1004             1|14         low Small-Large isolated
## 742           6  24      1004             1|14         low Small-Large isolated
## 743           6  24      1004             1|14         low Small-Large isolated
## 744           6  24      1004             1|14         low Small-Large isolated
## 745           7  28      1004             1|14         low Small-Large isolated
## 746           7  28      1004             1|14         low Small-Large isolated
## 747           7  28      1004             1|14         low Small-Large isolated
## 748           7  28      1004             1|14         low Small-Large isolated
## 749           7  28      1004             1|14         low Small-Large isolated
## 750           7  28      1004             1|14         low Small-Large isolated
## 751           7  28      1004             1|14         low Small-Large isolated
## 752           7  28      1004             1|14         low Small-Large isolated
## 753           7  28      1004             1|14         low Small-Large isolated
## 754           7  28      1004             1|14         low Small-Large isolated
## 755           7  28      1004             1|14         low Small-Large isolated
## 756           7  28      1004             1|14         low Small-Large isolated
## 757           7  28      1004             1|14         low Small-Large isolated
## 758           7  28      1004             1|14         low Small-Large isolated
## 759           7  28      1004             1|14         low Small-Large isolated
## 760           7  28      1004             1|14         low Small-Large isolated
## 761           7  28      1004             1|14         low Small-Large isolated
## 762           7  28      1004             1|14         low Small-Large isolated
## 763           7  28      1004             1|14         low Small-Large isolated
## 764           7  28      1004             1|14         low Small-Large isolated
## 765           7  28      1004             1|14         low Small-Large isolated
## 766           7  28      1004             1|14         low Small-Large isolated
## 767           7  28      1004             1|14         low Small-Large isolated
## 768           7  28      1004             1|14         low Small-Large isolated
## 769           0   0      1005             1|15         low Small-Large isolated
## 770           0   0      1005             1|15         low Small-Large isolated
## 771           0   0      1005             1|15         low Small-Large isolated
## 772           0   0      1005             1|15         low Small-Large isolated
## 773           0   0      1005             1|15         low Small-Large isolated
## 774           0   0      1005             1|15         low Small-Large isolated
## 775           0   0      1005             1|15         low Small-Large isolated
## 776           0   0      1005             1|15         low Small-Large isolated
## 777           0   0      1005             1|15         low Small-Large isolated
## 778           0   0      1005             1|15         low Small-Large isolated
## 779           0   0      1005             1|15         low Small-Large isolated
## 780           0   0      1005             1|15         low Small-Large isolated
## 781           0   0      1005             1|15         low Small-Large isolated
## 782           0   0      1005             1|15         low Small-Large isolated
## 783           0   0      1005             1|15         low Small-Large isolated
## 784           0   0      1005             1|15         low Small-Large isolated
## 785           0   0      1005             1|15         low Small-Large isolated
## 786           0   0      1005             1|15         low Small-Large isolated
## 787           0   0      1005             1|15         low Small-Large isolated
## 788           0   0      1005             1|15         low Small-Large isolated
## 789           0   0      1005             1|15         low Small-Large isolated
## 790           0   0      1005             1|15         low Small-Large isolated
## 791           0   0      1005             1|15         low Small-Large isolated
## 792           0   0      1005             1|15         low Small-Large isolated
## 793           1   4      1005             1|15         low Small-Large isolated
## 794           1   4      1005             1|15         low Small-Large isolated
## 795           1   4      1005             1|15         low Small-Large isolated
## 796           1   4      1005             1|15         low Small-Large isolated
## 797           1   4      1005             1|15         low Small-Large isolated
## 798           1   4      1005             1|15         low Small-Large isolated
## 799           1   4      1005             1|15         low Small-Large isolated
## 800           1   4      1005             1|15         low Small-Large isolated
## 801           1   4      1005             1|15         low Small-Large isolated
## 802           1   4      1005             1|15         low Small-Large isolated
## 803           1   4      1005             1|15         low Small-Large isolated
## 804           1   4      1005             1|15         low Small-Large isolated
## 805           1   4      1005             1|15         low Small-Large isolated
## 806           1   4      1005             1|15         low Small-Large isolated
## 807           1   4      1005             1|15         low Small-Large isolated
## 808           1   4      1005             1|15         low Small-Large isolated
## 809           1   4      1005             1|15         low Small-Large isolated
## 810           1   4      1005             1|15         low Small-Large isolated
## 811           1   4      1005             1|15         low Small-Large isolated
## 812           1   4      1005             1|15         low Small-Large isolated
## 813           1   4      1005             1|15         low Small-Large isolated
## 814           1   4      1005             1|15         low Small-Large isolated
## 815           1   4      1005             1|15         low Small-Large isolated
## 816           1   4      1005             1|15         low Small-Large isolated
## 817           2   8      1005             1|15         low Small-Large isolated
## 818           2   8      1005             1|15         low Small-Large isolated
## 819           2   8      1005             1|15         low Small-Large isolated
## 820           2   8      1005             1|15         low Small-Large isolated
## 821           2   8      1005             1|15         low Small-Large isolated
## 822           2   8      1005             1|15         low Small-Large isolated
## 823           2   8      1005             1|15         low Small-Large isolated
## 824           2   8      1005             1|15         low Small-Large isolated
## 825           2   8      1005             1|15         low Small-Large isolated
## 826           2   8      1005             1|15         low Small-Large isolated
## 827           2   8      1005             1|15         low Small-Large isolated
## 828           2   8      1005             1|15         low Small-Large isolated
## 829           2   8      1005             1|15         low Small-Large isolated
## 830           2   8      1005             1|15         low Small-Large isolated
## 831           2   8      1005             1|15         low Small-Large isolated
## 832           2   8      1005             1|15         low Small-Large isolated
## 833           2   8      1005             1|15         low Small-Large isolated
## 834           2   8      1005             1|15         low Small-Large isolated
## 835           2   8      1005             1|15         low Small-Large isolated
## 836           2   8      1005             1|15         low Small-Large isolated
## 837           2   8      1005             1|15         low Small-Large isolated
## 838           2   8      1005             1|15         low Small-Large isolated
## 839           2   8      1005             1|15         low Small-Large isolated
## 840           2   8      1005             1|15         low Small-Large isolated
## 841           3  12      1005             1|15         low Small-Large isolated
## 842           3  12      1005             1|15         low Small-Large isolated
## 843           3  12      1005             1|15         low Small-Large isolated
## 844           3  12      1005             1|15         low Small-Large isolated
## 845           3  12      1005             1|15         low Small-Large isolated
## 846           3  12      1005             1|15         low Small-Large isolated
## 847           3  12      1005             1|15         low Small-Large isolated
## 848           3  12      1005             1|15         low Small-Large isolated
## 849           3  12      1005             1|15         low Small-Large isolated
## 850           3  12      1005             1|15         low Small-Large isolated
## 851           3  12      1005             1|15         low Small-Large isolated
## 852           3  12      1005             1|15         low Small-Large isolated
## 853           3  12      1005             1|15         low Small-Large isolated
## 854           3  12      1005             1|15         low Small-Large isolated
## 855           3  12      1005             1|15         low Small-Large isolated
## 856           3  12      1005             1|15         low Small-Large isolated
## 857           3  12      1005             1|15         low Small-Large isolated
## 858           3  12      1005             1|15         low Small-Large isolated
## 859           3  12      1005             1|15         low Small-Large isolated
## 860           3  12      1005             1|15         low Small-Large isolated
## 861           3  12      1005             1|15         low Small-Large isolated
## 862           3  12      1005             1|15         low Small-Large isolated
## 863           3  12      1005             1|15         low Small-Large isolated
## 864           3  12      1005             1|15         low Small-Large isolated
## 865           4  16      1005             1|15         low Small-Large isolated
## 866           4  16      1005             1|15         low Small-Large isolated
## 867           4  16      1005             1|15         low Small-Large isolated
## 868           4  16      1005             1|15         low Small-Large isolated
## 869           4  16      1005             1|15         low Small-Large isolated
## 870           4  16      1005             1|15         low Small-Large isolated
## 871           4  16      1005             1|15         low Small-Large isolated
## 872           4  16      1005             1|15         low Small-Large isolated
## 873           4  16      1005             1|15         low Small-Large isolated
## 874           4  16      1005             1|15         low Small-Large isolated
## 875           4  16      1005             1|15         low Small-Large isolated
## 876           4  16      1005             1|15         low Small-Large isolated
## 877           4  16      1005             1|15         low Small-Large isolated
## 878           4  16      1005             1|15         low Small-Large isolated
## 879           4  16      1005             1|15         low Small-Large isolated
## 880           4  16      1005             1|15         low Small-Large isolated
## 881           4  16      1005             1|15         low Small-Large isolated
## 882           4  16      1005             1|15         low Small-Large isolated
## 883           4  16      1005             1|15         low Small-Large isolated
## 884           4  16      1005             1|15         low Small-Large isolated
## 885           4  16      1005             1|15         low Small-Large isolated
## 886           4  16      1005             1|15         low Small-Large isolated
## 887           4  16      1005             1|15         low Small-Large isolated
## 888           4  16      1005             1|15         low Small-Large isolated
## 889           5  20      1005             1|15         low Small-Large isolated
## 890           5  20      1005             1|15         low Small-Large isolated
## 891           5  20      1005             1|15         low Small-Large isolated
## 892           5  20      1005             1|15         low Small-Large isolated
## 893           5  20      1005             1|15         low Small-Large isolated
## 894           5  20      1005             1|15         low Small-Large isolated
## 895           5  20      1005             1|15         low Small-Large isolated
## 896           5  20      1005             1|15         low Small-Large isolated
## 897           5  20      1005             1|15         low Small-Large isolated
## 898           5  20      1005             1|15         low Small-Large isolated
## 899           5  20      1005             1|15         low Small-Large isolated
## 900           5  20      1005             1|15         low Small-Large isolated
## 901           5  20      1005             1|15         low Small-Large isolated
## 902           5  20      1005             1|15         low Small-Large isolated
## 903           5  20      1005             1|15         low Small-Large isolated
## 904           5  20      1005             1|15         low Small-Large isolated
## 905           5  20      1005             1|15         low Small-Large isolated
## 906           5  20      1005             1|15         low Small-Large isolated
## 907           5  20      1005             1|15         low Small-Large isolated
## 908           5  20      1005             1|15         low Small-Large isolated
## 909           5  20      1005             1|15         low Small-Large isolated
## 910           5  20      1005             1|15         low Small-Large isolated
## 911           5  20      1005             1|15         low Small-Large isolated
## 912           5  20      1005             1|15         low Small-Large isolated
## 913           6  24      1005             1|15         low Small-Large isolated
## 914           6  24      1005             1|15         low Small-Large isolated
## 915           6  24      1005             1|15         low Small-Large isolated
## 916           6  24      1005             1|15         low Small-Large isolated
## 917           6  24      1005             1|15         low Small-Large isolated
## 918           6  24      1005             1|15         low Small-Large isolated
## 919           6  24      1005             1|15         low Small-Large isolated
## 920           6  24      1005             1|15         low Small-Large isolated
## 921           6  24      1005             1|15         low Small-Large isolated
## 922           6  24      1005             1|15         low Small-Large isolated
## 923           6  24      1005             1|15         low Small-Large isolated
## 924           6  24      1005             1|15         low Small-Large isolated
## 925           6  24      1005             1|15         low Small-Large isolated
## 926           6  24      1005             1|15         low Small-Large isolated
## 927           6  24      1005             1|15         low Small-Large isolated
## 928           6  24      1005             1|15         low Small-Large isolated
## 929           6  24      1005             1|15         low Small-Large isolated
## 930           6  24      1005             1|15         low Small-Large isolated
## 931           6  24      1005             1|15         low Small-Large isolated
## 932           6  24      1005             1|15         low Small-Large isolated
## 933           6  24      1005             1|15         low Small-Large isolated
## 934           6  24      1005             1|15         low Small-Large isolated
## 935           6  24      1005             1|15         low Small-Large isolated
## 936           6  24      1005             1|15         low Small-Large isolated
## 937           7  28      1005             1|15         low Small-Large isolated
## 938           7  28      1005             1|15         low Small-Large isolated
## 939           7  28      1005             1|15         low Small-Large isolated
## 940           7  28      1005             1|15         low Small-Large isolated
## 941           7  28      1005             1|15         low Small-Large isolated
## 942           7  28      1005             1|15         low Small-Large isolated
## 943           7  28      1005             1|15         low Small-Large isolated
## 944           7  28      1005             1|15         low Small-Large isolated
## 945           7  28      1005             1|15         low Small-Large isolated
## 946           7  28      1005             1|15         low Small-Large isolated
## 947           7  28      1005             1|15         low Small-Large isolated
## 948           7  28      1005             1|15         low Small-Large isolated
## 949           7  28      1005             1|15         low Small-Large isolated
## 950           7  28      1005             1|15         low Small-Large isolated
## 951           7  28      1005             1|15         low Small-Large isolated
## 952           7  28      1005             1|15         low Small-Large isolated
## 953           7  28      1005             1|15         low Small-Large isolated
## 954           7  28      1005             1|15         low Small-Large isolated
## 955           7  28      1005             1|15         low Small-Large isolated
## 956           7  28      1005             1|15         low Small-Large isolated
## 957           7  28      1005             1|15         low Small-Large isolated
## 958           7  28      1005             1|15         low Small-Large isolated
## 959           7  28      1005             1|15         low Small-Large isolated
## 960           7  28      1005             1|15         low Small-Large isolated
## 961           0   0      1006             2|11         low Small-Large isolated
## 962           0   0      1006             2|11         low Small-Large isolated
## 963           0   0      1006             2|11         low Small-Large isolated
## 964           0   0      1006             2|11         low Small-Large isolated
## 965           0   0      1006             2|11         low Small-Large isolated
## 966           0   0      1006             2|11         low Small-Large isolated
## 967           0   0      1006             2|11         low Small-Large isolated
## 968           0   0      1006             2|11         low Small-Large isolated
## 969           0   0      1006             2|11         low Small-Large isolated
## 970           0   0      1006             2|11         low Small-Large isolated
## 971           0   0      1006             2|11         low Small-Large isolated
## 972           0   0      1006             2|11         low Small-Large isolated
## 973           0   0      1006             2|11         low Small-Large isolated
## 974           0   0      1006             2|11         low Small-Large isolated
## 975           0   0      1006             2|11         low Small-Large isolated
## 976           0   0      1006             2|11         low Small-Large isolated
## 977           0   0      1006             2|11         low Small-Large isolated
## 978           0   0      1006             2|11         low Small-Large isolated
## 979           0   0      1006             2|11         low Small-Large isolated
## 980           0   0      1006             2|11         low Small-Large isolated
## 981           0   0      1006             2|11         low Small-Large isolated
## 982           0   0      1006             2|11         low Small-Large isolated
## 983           0   0      1006             2|11         low Small-Large isolated
## 984           0   0      1006             2|11         low Small-Large isolated
## 985           1   4      1006             2|11         low Small-Large isolated
## 986           1   4      1006             2|11         low Small-Large isolated
## 987           1   4      1006             2|11         low Small-Large isolated
## 988           1   4      1006             2|11         low Small-Large isolated
## 989           1   4      1006             2|11         low Small-Large isolated
## 990           1   4      1006             2|11         low Small-Large isolated
## 991           1   4      1006             2|11         low Small-Large isolated
## 992           1   4      1006             2|11         low Small-Large isolated
## 993           1   4      1006             2|11         low Small-Large isolated
## 994           1   4      1006             2|11         low Small-Large isolated
## 995           1   4      1006             2|11         low Small-Large isolated
## 996           1   4      1006             2|11         low Small-Large isolated
## 997           1   4      1006             2|11         low Small-Large isolated
## 998           1   4      1006             2|11         low Small-Large isolated
## 999           1   4      1006             2|11         low Small-Large isolated
## 1000          1   4      1006             2|11         low Small-Large isolated
## 1001          1   4      1006             2|11         low Small-Large isolated
## 1002          1   4      1006             2|11         low Small-Large isolated
## 1003          1   4      1006             2|11         low Small-Large isolated
## 1004          1   4      1006             2|11         low Small-Large isolated
## 1005          1   4      1006             2|11         low Small-Large isolated
## 1006          1   4      1006             2|11         low Small-Large isolated
## 1007          1   4      1006             2|11         low Small-Large isolated
## 1008          1   4      1006             2|11         low Small-Large isolated
## 1009          2   8      1006             2|11         low Small-Large isolated
## 1010          2   8      1006             2|11         low Small-Large isolated
## 1011          2   8      1006             2|11         low Small-Large isolated
## 1012          2   8      1006             2|11         low Small-Large isolated
## 1013          2   8      1006             2|11         low Small-Large isolated
## 1014          2   8      1006             2|11         low Small-Large isolated
## 1015          2   8      1006             2|11         low Small-Large isolated
## 1016          2   8      1006             2|11         low Small-Large isolated
## 1017          2   8      1006             2|11         low Small-Large isolated
## 1018          2   8      1006             2|11         low Small-Large isolated
## 1019          2   8      1006             2|11         low Small-Large isolated
## 1020          2   8      1006             2|11         low Small-Large isolated
## 1021          2   8      1006             2|11         low Small-Large isolated
## 1022          2   8      1006             2|11         low Small-Large isolated
## 1023          2   8      1006             2|11         low Small-Large isolated
## 1024          2   8      1006             2|11         low Small-Large isolated
## 1025          2   8      1006             2|11         low Small-Large isolated
## 1026          2   8      1006             2|11         low Small-Large isolated
## 1027          2   8      1006             2|11         low Small-Large isolated
## 1028          2   8      1006             2|11         low Small-Large isolated
## 1029          2   8      1006             2|11         low Small-Large isolated
## 1030          2   8      1006             2|11         low Small-Large isolated
## 1031          2   8      1006             2|11         low Small-Large isolated
## 1032          2   8      1006             2|11         low Small-Large isolated
## 1033          3  12      1006             2|11         low Small-Large isolated
## 1034          3  12      1006             2|11         low Small-Large isolated
## 1035          3  12      1006             2|11         low Small-Large isolated
## 1036          3  12      1006             2|11         low Small-Large isolated
## 1037          3  12      1006             2|11         low Small-Large isolated
## 1038          3  12      1006             2|11         low Small-Large isolated
## 1039          3  12      1006             2|11         low Small-Large isolated
## 1040          3  12      1006             2|11         low Small-Large isolated
## 1041          3  12      1006             2|11         low Small-Large isolated
## 1042          3  12      1006             2|11         low Small-Large isolated
## 1043          3  12      1006             2|11         low Small-Large isolated
## 1044          3  12      1006             2|11         low Small-Large isolated
## 1045          3  12      1006             2|11         low Small-Large isolated
## 1046          3  12      1006             2|11         low Small-Large isolated
## 1047          3  12      1006             2|11         low Small-Large isolated
## 1048          3  12      1006             2|11         low Small-Large isolated
## 1049          3  12      1006             2|11         low Small-Large isolated
## 1050          3  12      1006             2|11         low Small-Large isolated
## 1051          3  12      1006             2|11         low Small-Large isolated
## 1052          3  12      1006             2|11         low Small-Large isolated
## 1053          3  12      1006             2|11         low Small-Large isolated
## 1054          3  12      1006             2|11         low Small-Large isolated
## 1055          3  12      1006             2|11         low Small-Large isolated
## 1056          3  12      1006             2|11         low Small-Large isolated
## 1057          4  16      1006             2|11         low Small-Large isolated
## 1058          4  16      1006             2|11         low Small-Large isolated
## 1059          4  16      1006             2|11         low Small-Large isolated
## 1060          4  16      1006             2|11         low Small-Large isolated
## 1061          4  16      1006             2|11         low Small-Large isolated
## 1062          4  16      1006             2|11         low Small-Large isolated
## 1063          4  16      1006             2|11         low Small-Large isolated
## 1064          4  16      1006             2|11         low Small-Large isolated
## 1065          4  16      1006             2|11         low Small-Large isolated
## 1066          4  16      1006             2|11         low Small-Large isolated
## 1067          4  16      1006             2|11         low Small-Large isolated
## 1068          4  16      1006             2|11         low Small-Large isolated
## 1069          4  16      1006             2|11         low Small-Large isolated
## 1070          4  16      1006             2|11         low Small-Large isolated
## 1071          4  16      1006             2|11         low Small-Large isolated
## 1072          4  16      1006             2|11         low Small-Large isolated
## 1073          4  16      1006             2|11         low Small-Large isolated
## 1074          4  16      1006             2|11         low Small-Large isolated
## 1075          4  16      1006             2|11         low Small-Large isolated
## 1076          4  16      1006             2|11         low Small-Large isolated
## 1077          4  16      1006             2|11         low Small-Large isolated
## 1078          4  16      1006             2|11         low Small-Large isolated
## 1079          4  16      1006             2|11         low Small-Large isolated
## 1080          4  16      1006             2|11         low Small-Large isolated
## 1081          5  20      1006             2|11         low Small-Large isolated
## 1082          5  20      1006             2|11         low Small-Large isolated
## 1083          5  20      1006             2|11         low Small-Large isolated
## 1084          5  20      1006             2|11         low Small-Large isolated
## 1085          5  20      1006             2|11         low Small-Large isolated
## 1086          5  20      1006             2|11         low Small-Large isolated
## 1087          5  20      1006             2|11         low Small-Large isolated
## 1088          5  20      1006             2|11         low Small-Large isolated
## 1089          5  20      1006             2|11         low Small-Large isolated
## 1090          5  20      1006             2|11         low Small-Large isolated
## 1091          5  20      1006             2|11         low Small-Large isolated
## 1092          5  20      1006             2|11         low Small-Large isolated
## 1093          5  20      1006             2|11         low Small-Large isolated
## 1094          5  20      1006             2|11         low Small-Large isolated
## 1095          5  20      1006             2|11         low Small-Large isolated
## 1096          5  20      1006             2|11         low Small-Large isolated
## 1097          5  20      1006             2|11         low Small-Large isolated
## 1098          5  20      1006             2|11         low Small-Large isolated
## 1099          5  20      1006             2|11         low Small-Large isolated
## 1100          5  20      1006             2|11         low Small-Large isolated
## 1101          5  20      1006             2|11         low Small-Large isolated
## 1102          5  20      1006             2|11         low Small-Large isolated
## 1103          5  20      1006             2|11         low Small-Large isolated
## 1104          5  20      1006             2|11         low Small-Large isolated
## 1105          6  24      1006             2|11         low Small-Large isolated
## 1106          6  24      1006             2|11         low Small-Large isolated
## 1107          6  24      1006             2|11         low Small-Large isolated
## 1108          6  24      1006             2|11         low Small-Large isolated
## 1109          6  24      1006             2|11         low Small-Large isolated
## 1110          6  24      1006             2|11         low Small-Large isolated
## 1111          6  24      1006             2|11         low Small-Large isolated
## 1112          6  24      1006             2|11         low Small-Large isolated
## 1113          6  24      1006             2|11         low Small-Large isolated
## 1114          6  24      1006             2|11         low Small-Large isolated
## 1115          6  24      1006             2|11         low Small-Large isolated
## 1116          6  24      1006             2|11         low Small-Large isolated
## 1117          6  24      1006             2|11         low Small-Large isolated
## 1118          6  24      1006             2|11         low Small-Large isolated
## 1119          6  24      1006             2|11         low Small-Large isolated
## 1120          6  24      1006             2|11         low Small-Large isolated
## 1121          6  24      1006             2|11         low Small-Large isolated
## 1122          6  24      1006             2|11         low Small-Large isolated
## 1123          6  24      1006             2|11         low Small-Large isolated
## 1124          6  24      1006             2|11         low Small-Large isolated
## 1125          6  24      1006             2|11         low Small-Large isolated
## 1126          6  24      1006             2|11         low Small-Large isolated
## 1127          6  24      1006             2|11         low Small-Large isolated
## 1128          6  24      1006             2|11         low Small-Large isolated
## 1129          7  28      1006             2|11         low Small-Large isolated
## 1130          7  28      1006             2|11         low Small-Large isolated
## 1131          7  28      1006             2|11         low Small-Large isolated
## 1132          7  28      1006             2|11         low Small-Large isolated
## 1133          7  28      1006             2|11         low Small-Large isolated
## 1134          7  28      1006             2|11         low Small-Large isolated
## 1135          7  28      1006             2|11         low Small-Large isolated
## 1136          7  28      1006             2|11         low Small-Large isolated
## 1137          7  28      1006             2|11         low Small-Large isolated
## 1138          7  28      1006             2|11         low Small-Large isolated
## 1139          7  28      1006             2|11         low Small-Large isolated
## 1140          7  28      1006             2|11         low Small-Large isolated
## 1141          7  28      1006             2|11         low Small-Large isolated
## 1142          7  28      1006             2|11         low Small-Large isolated
## 1143          7  28      1006             2|11         low Small-Large isolated
## 1144          7  28      1006             2|11         low Small-Large isolated
## 1145          7  28      1006             2|11         low Small-Large isolated
## 1146          7  28      1006             2|11         low Small-Large isolated
## 1147          7  28      1006             2|11         low Small-Large isolated
## 1148          7  28      1006             2|11         low Small-Large isolated
## 1149          7  28      1006             2|11         low Small-Large isolated
## 1150          7  28      1006             2|11         low Small-Large isolated
## 1151          7  28      1006             2|11         low Small-Large isolated
## 1152          7  28      1006             2|11         low Small-Large isolated
## 1153          0   0      1007             2|12         low Small-Large isolated
## 1154          0   0      1007             2|12         low Small-Large isolated
## 1155          0   0      1007             2|12         low Small-Large isolated
## 1156          0   0      1007             2|12         low Small-Large isolated
## 1157          0   0      1007             2|12         low Small-Large isolated
## 1158          0   0      1007             2|12         low Small-Large isolated
## 1159          0   0      1007             2|12         low Small-Large isolated
## 1160          0   0      1007             2|12         low Small-Large isolated
## 1161          0   0      1007             2|12         low Small-Large isolated
## 1162          0   0      1007             2|12         low Small-Large isolated
## 1163          0   0      1007             2|12         low Small-Large isolated
## 1164          0   0      1007             2|12         low Small-Large isolated
## 1165          0   0      1007             2|12         low Small-Large isolated
## 1166          0   0      1007             2|12         low Small-Large isolated
## 1167          0   0      1007             2|12         low Small-Large isolated
## 1168          0   0      1007             2|12         low Small-Large isolated
## 1169          0   0      1007             2|12         low Small-Large isolated
## 1170          0   0      1007             2|12         low Small-Large isolated
## 1171          0   0      1007             2|12         low Small-Large isolated
## 1172          0   0      1007             2|12         low Small-Large isolated
## 1173          0   0      1007             2|12         low Small-Large isolated
## 1174          0   0      1007             2|12         low Small-Large isolated
## 1175          0   0      1007             2|12         low Small-Large isolated
## 1176          0   0      1007             2|12         low Small-Large isolated
## 1177          1   4      1007             2|12         low Small-Large isolated
## 1178          1   4      1007             2|12         low Small-Large isolated
## 1179          1   4      1007             2|12         low Small-Large isolated
## 1180          1   4      1007             2|12         low Small-Large isolated
## 1181          1   4      1007             2|12         low Small-Large isolated
## 1182          1   4      1007             2|12         low Small-Large isolated
## 1183          1   4      1007             2|12         low Small-Large isolated
## 1184          1   4      1007             2|12         low Small-Large isolated
## 1185          1   4      1007             2|12         low Small-Large isolated
## 1186          1   4      1007             2|12         low Small-Large isolated
## 1187          1   4      1007             2|12         low Small-Large isolated
## 1188          1   4      1007             2|12         low Small-Large isolated
## 1189          1   4      1007             2|12         low Small-Large isolated
## 1190          1   4      1007             2|12         low Small-Large isolated
## 1191          1   4      1007             2|12         low Small-Large isolated
## 1192          1   4      1007             2|12         low Small-Large isolated
## 1193          1   4      1007             2|12         low Small-Large isolated
## 1194          1   4      1007             2|12         low Small-Large isolated
## 1195          1   4      1007             2|12         low Small-Large isolated
## 1196          1   4      1007             2|12         low Small-Large isolated
## 1197          1   4      1007             2|12         low Small-Large isolated
## 1198          1   4      1007             2|12         low Small-Large isolated
## 1199          1   4      1007             2|12         low Small-Large isolated
## 1200          1   4      1007             2|12         low Small-Large isolated
## 1201          2   8      1007             2|12         low Small-Large isolated
## 1202          2   8      1007             2|12         low Small-Large isolated
## 1203          2   8      1007             2|12         low Small-Large isolated
## 1204          2   8      1007             2|12         low Small-Large isolated
## 1205          2   8      1007             2|12         low Small-Large isolated
## 1206          2   8      1007             2|12         low Small-Large isolated
## 1207          2   8      1007             2|12         low Small-Large isolated
## 1208          2   8      1007             2|12         low Small-Large isolated
## 1209          2   8      1007             2|12         low Small-Large isolated
## 1210          2   8      1007             2|12         low Small-Large isolated
## 1211          2   8      1007             2|12         low Small-Large isolated
## 1212          2   8      1007             2|12         low Small-Large isolated
## 1213          2   8      1007             2|12         low Small-Large isolated
## 1214          2   8      1007             2|12         low Small-Large isolated
## 1215          2   8      1007             2|12         low Small-Large isolated
## 1216          2   8      1007             2|12         low Small-Large isolated
## 1217          2   8      1007             2|12         low Small-Large isolated
## 1218          2   8      1007             2|12         low Small-Large isolated
## 1219          2   8      1007             2|12         low Small-Large isolated
## 1220          2   8      1007             2|12         low Small-Large isolated
## 1221          2   8      1007             2|12         low Small-Large isolated
## 1222          2   8      1007             2|12         low Small-Large isolated
## 1223          2   8      1007             2|12         low Small-Large isolated
## 1224          2   8      1007             2|12         low Small-Large isolated
## 1225          3  12      1007             2|12         low Small-Large isolated
## 1226          3  12      1007             2|12         low Small-Large isolated
## 1227          3  12      1007             2|12         low Small-Large isolated
## 1228          3  12      1007             2|12         low Small-Large isolated
## 1229          3  12      1007             2|12         low Small-Large isolated
## 1230          3  12      1007             2|12         low Small-Large isolated
## 1231          3  12      1007             2|12         low Small-Large isolated
## 1232          3  12      1007             2|12         low Small-Large isolated
## 1233          3  12      1007             2|12         low Small-Large isolated
## 1234          3  12      1007             2|12         low Small-Large isolated
## 1235          3  12      1007             2|12         low Small-Large isolated
## 1236          3  12      1007             2|12         low Small-Large isolated
## 1237          3  12      1007             2|12         low Small-Large isolated
## 1238          3  12      1007             2|12         low Small-Large isolated
## 1239          3  12      1007             2|12         low Small-Large isolated
## 1240          3  12      1007             2|12         low Small-Large isolated
## 1241          3  12      1007             2|12         low Small-Large isolated
## 1242          3  12      1007             2|12         low Small-Large isolated
## 1243          3  12      1007             2|12         low Small-Large isolated
## 1244          3  12      1007             2|12         low Small-Large isolated
## 1245          3  12      1007             2|12         low Small-Large isolated
## 1246          3  12      1007             2|12         low Small-Large isolated
## 1247          3  12      1007             2|12         low Small-Large isolated
## 1248          3  12      1007             2|12         low Small-Large isolated
## 1249          4  16      1007             2|12         low Small-Large isolated
## 1250          4  16      1007             2|12         low Small-Large isolated
## 1251          4  16      1007             2|12         low Small-Large isolated
## 1252          4  16      1007             2|12         low Small-Large isolated
## 1253          4  16      1007             2|12         low Small-Large isolated
## 1254          4  16      1007             2|12         low Small-Large isolated
## 1255          4  16      1007             2|12         low Small-Large isolated
## 1256          4  16      1007             2|12         low Small-Large isolated
## 1257          4  16      1007             2|12         low Small-Large isolated
## 1258          4  16      1007             2|12         low Small-Large isolated
## 1259          4  16      1007             2|12         low Small-Large isolated
## 1260          4  16      1007             2|12         low Small-Large isolated
## 1261          4  16      1007             2|12         low Small-Large isolated
## 1262          4  16      1007             2|12         low Small-Large isolated
## 1263          4  16      1007             2|12         low Small-Large isolated
## 1264          4  16      1007             2|12         low Small-Large isolated
## 1265          4  16      1007             2|12         low Small-Large isolated
## 1266          4  16      1007             2|12         low Small-Large isolated
## 1267          4  16      1007             2|12         low Small-Large isolated
## 1268          4  16      1007             2|12         low Small-Large isolated
## 1269          4  16      1007             2|12         low Small-Large isolated
## 1270          4  16      1007             2|12         low Small-Large isolated
## 1271          4  16      1007             2|12         low Small-Large isolated
## 1272          4  16      1007             2|12         low Small-Large isolated
## 1273          5  20      1007             2|12         low Small-Large isolated
## 1274          5  20      1007             2|12         low Small-Large isolated
## 1275          5  20      1007             2|12         low Small-Large isolated
## 1276          5  20      1007             2|12         low Small-Large isolated
## 1277          5  20      1007             2|12         low Small-Large isolated
## 1278          5  20      1007             2|12         low Small-Large isolated
## 1279          5  20      1007             2|12         low Small-Large isolated
## 1280          5  20      1007             2|12         low Small-Large isolated
## 1281          5  20      1007             2|12         low Small-Large isolated
## 1282          5  20      1007             2|12         low Small-Large isolated
## 1283          5  20      1007             2|12         low Small-Large isolated
## 1284          5  20      1007             2|12         low Small-Large isolated
## 1285          5  20      1007             2|12         low Small-Large isolated
## 1286          5  20      1007             2|12         low Small-Large isolated
## 1287          5  20      1007             2|12         low Small-Large isolated
## 1288          5  20      1007             2|12         low Small-Large isolated
## 1289          5  20      1007             2|12         low Small-Large isolated
## 1290          5  20      1007             2|12         low Small-Large isolated
## 1291          5  20      1007             2|12         low Small-Large isolated
## 1292          5  20      1007             2|12         low Small-Large isolated
## 1293          5  20      1007             2|12         low Small-Large isolated
## 1294          5  20      1007             2|12         low Small-Large isolated
## 1295          5  20      1007             2|12         low Small-Large isolated
## 1296          5  20      1007             2|12         low Small-Large isolated
## 1297          6  24      1007             2|12         low Small-Large isolated
## 1298          6  24      1007             2|12         low Small-Large isolated
## 1299          6  24      1007             2|12         low Small-Large isolated
## 1300          6  24      1007             2|12         low Small-Large isolated
## 1301          6  24      1007             2|12         low Small-Large isolated
## 1302          6  24      1007             2|12         low Small-Large isolated
## 1303          6  24      1007             2|12         low Small-Large isolated
## 1304          6  24      1007             2|12         low Small-Large isolated
## 1305          6  24      1007             2|12         low Small-Large isolated
## 1306          6  24      1007             2|12         low Small-Large isolated
## 1307          6  24      1007             2|12         low Small-Large isolated
## 1308          6  24      1007             2|12         low Small-Large isolated
## 1309          6  24      1007             2|12         low Small-Large isolated
## 1310          6  24      1007             2|12         low Small-Large isolated
## 1311          6  24      1007             2|12         low Small-Large isolated
## 1312          6  24      1007             2|12         low Small-Large isolated
## 1313          6  24      1007             2|12         low Small-Large isolated
## 1314          6  24      1007             2|12         low Small-Large isolated
## 1315          6  24      1007             2|12         low Small-Large isolated
## 1316          6  24      1007             2|12         low Small-Large isolated
## 1317          6  24      1007             2|12         low Small-Large isolated
## 1318          6  24      1007             2|12         low Small-Large isolated
## 1319          6  24      1007             2|12         low Small-Large isolated
## 1320          6  24      1007             2|12         low Small-Large isolated
## 1321          7  28      1007             2|12         low Small-Large isolated
## 1322          7  28      1007             2|12         low Small-Large isolated
## 1323          7  28      1007             2|12         low Small-Large isolated
## 1324          7  28      1007             2|12         low Small-Large isolated
## 1325          7  28      1007             2|12         low Small-Large isolated
## 1326          7  28      1007             2|12         low Small-Large isolated
## 1327          7  28      1007             2|12         low Small-Large isolated
## 1328          7  28      1007             2|12         low Small-Large isolated
## 1329          7  28      1007             2|12         low Small-Large isolated
## 1330          7  28      1007             2|12         low Small-Large isolated
## 1331          7  28      1007             2|12         low Small-Large isolated
## 1332          7  28      1007             2|12         low Small-Large isolated
## 1333          7  28      1007             2|12         low Small-Large isolated
## 1334          7  28      1007             2|12         low Small-Large isolated
## 1335          7  28      1007             2|12         low Small-Large isolated
## 1336          7  28      1007             2|12         low Small-Large isolated
## 1337          7  28      1007             2|12         low Small-Large isolated
## 1338          7  28      1007             2|12         low Small-Large isolated
## 1339          7  28      1007             2|12         low Small-Large isolated
## 1340          7  28      1007             2|12         low Small-Large isolated
## 1341          7  28      1007             2|12         low Small-Large isolated
## 1342          7  28      1007             2|12         low Small-Large isolated
## 1343          7  28      1007             2|12         low Small-Large isolated
## 1344          7  28      1007             2|12         low Small-Large isolated
## 1345          0   0      1008             2|13         low Small-Large isolated
## 1346          0   0      1008             2|13         low Small-Large isolated
## 1347          0   0      1008             2|13         low Small-Large isolated
## 1348          0   0      1008             2|13         low Small-Large isolated
## 1349          0   0      1008             2|13         low Small-Large isolated
## 1350          0   0      1008             2|13         low Small-Large isolated
## 1351          0   0      1008             2|13         low Small-Large isolated
## 1352          0   0      1008             2|13         low Small-Large isolated
## 1353          0   0      1008             2|13         low Small-Large isolated
## 1354          0   0      1008             2|13         low Small-Large isolated
## 1355          0   0      1008             2|13         low Small-Large isolated
## 1356          0   0      1008             2|13         low Small-Large isolated
## 1357          0   0      1008             2|13         low Small-Large isolated
## 1358          0   0      1008             2|13         low Small-Large isolated
## 1359          0   0      1008             2|13         low Small-Large isolated
## 1360          0   0      1008             2|13         low Small-Large isolated
## 1361          0   0      1008             2|13         low Small-Large isolated
## 1362          0   0      1008             2|13         low Small-Large isolated
## 1363          0   0      1008             2|13         low Small-Large isolated
## 1364          0   0      1008             2|13         low Small-Large isolated
## 1365          0   0      1008             2|13         low Small-Large isolated
## 1366          0   0      1008             2|13         low Small-Large isolated
## 1367          0   0      1008             2|13         low Small-Large isolated
## 1368          0   0      1008             2|13         low Small-Large isolated
## 1369          1   4      1008             2|13         low Small-Large isolated
## 1370          1   4      1008             2|13         low Small-Large isolated
## 1371          1   4      1008             2|13         low Small-Large isolated
## 1372          1   4      1008             2|13         low Small-Large isolated
## 1373          1   4      1008             2|13         low Small-Large isolated
## 1374          1   4      1008             2|13         low Small-Large isolated
## 1375          1   4      1008             2|13         low Small-Large isolated
## 1376          1   4      1008             2|13         low Small-Large isolated
## 1377          1   4      1008             2|13         low Small-Large isolated
## 1378          1   4      1008             2|13         low Small-Large isolated
## 1379          1   4      1008             2|13         low Small-Large isolated
## 1380          1   4      1008             2|13         low Small-Large isolated
## 1381          1   4      1008             2|13         low Small-Large isolated
## 1382          1   4      1008             2|13         low Small-Large isolated
## 1383          1   4      1008             2|13         low Small-Large isolated
## 1384          1   4      1008             2|13         low Small-Large isolated
## 1385          1   4      1008             2|13         low Small-Large isolated
## 1386          1   4      1008             2|13         low Small-Large isolated
## 1387          1   4      1008             2|13         low Small-Large isolated
## 1388          1   4      1008             2|13         low Small-Large isolated
## 1389          1   4      1008             2|13         low Small-Large isolated
## 1390          1   4      1008             2|13         low Small-Large isolated
## 1391          1   4      1008             2|13         low Small-Large isolated
## 1392          1   4      1008             2|13         low Small-Large isolated
## 1393          2   8      1008             2|13         low Small-Large isolated
## 1394          2   8      1008             2|13         low Small-Large isolated
## 1395          2   8      1008             2|13         low Small-Large isolated
## 1396          2   8      1008             2|13         low Small-Large isolated
## 1397          2   8      1008             2|13         low Small-Large isolated
## 1398          2   8      1008             2|13         low Small-Large isolated
## 1399          2   8      1008             2|13         low Small-Large isolated
## 1400          2   8      1008             2|13         low Small-Large isolated
## 1401          2   8      1008             2|13         low Small-Large isolated
## 1402          2   8      1008             2|13         low Small-Large isolated
## 1403          2   8      1008             2|13         low Small-Large isolated
## 1404          2   8      1008             2|13         low Small-Large isolated
## 1405          2   8      1008             2|13         low Small-Large isolated
## 1406          2   8      1008             2|13         low Small-Large isolated
## 1407          2   8      1008             2|13         low Small-Large isolated
## 1408          2   8      1008             2|13         low Small-Large isolated
## 1409          2   8      1008             2|13         low Small-Large isolated
## 1410          2   8      1008             2|13         low Small-Large isolated
## 1411          2   8      1008             2|13         low Small-Large isolated
## 1412          2   8      1008             2|13         low Small-Large isolated
## 1413          2   8      1008             2|13         low Small-Large isolated
## 1414          2   8      1008             2|13         low Small-Large isolated
## 1415          2   8      1008             2|13         low Small-Large isolated
## 1416          2   8      1008             2|13         low Small-Large isolated
## 1417          3  12      1008             2|13         low Small-Large isolated
## 1418          3  12      1008             2|13         low Small-Large isolated
## 1419          3  12      1008             2|13         low Small-Large isolated
## 1420          3  12      1008             2|13         low Small-Large isolated
## 1421          3  12      1008             2|13         low Small-Large isolated
## 1422          3  12      1008             2|13         low Small-Large isolated
## 1423          3  12      1008             2|13         low Small-Large isolated
## 1424          3  12      1008             2|13         low Small-Large isolated
## 1425          3  12      1008             2|13         low Small-Large isolated
## 1426          3  12      1008             2|13         low Small-Large isolated
## 1427          3  12      1008             2|13         low Small-Large isolated
## 1428          3  12      1008             2|13         low Small-Large isolated
## 1429          3  12      1008             2|13         low Small-Large isolated
## 1430          3  12      1008             2|13         low Small-Large isolated
## 1431          3  12      1008             2|13         low Small-Large isolated
## 1432          3  12      1008             2|13         low Small-Large isolated
## 1433          3  12      1008             2|13         low Small-Large isolated
## 1434          3  12      1008             2|13         low Small-Large isolated
## 1435          3  12      1008             2|13         low Small-Large isolated
## 1436          3  12      1008             2|13         low Small-Large isolated
## 1437          3  12      1008             2|13         low Small-Large isolated
## 1438          3  12      1008             2|13         low Small-Large isolated
## 1439          3  12      1008             2|13         low Small-Large isolated
## 1440          3  12      1008             2|13         low Small-Large isolated
## 1441          4  16      1008             2|13         low Small-Large isolated
## 1442          4  16      1008             2|13         low Small-Large isolated
## 1443          4  16      1008             2|13         low Small-Large isolated
## 1444          4  16      1008             2|13         low Small-Large isolated
## 1445          4  16      1008             2|13         low Small-Large isolated
## 1446          4  16      1008             2|13         low Small-Large isolated
## 1447          4  16      1008             2|13         low Small-Large isolated
## 1448          4  16      1008             2|13         low Small-Large isolated
## 1449          4  16      1008             2|13         low Small-Large isolated
## 1450          4  16      1008             2|13         low Small-Large isolated
## 1451          4  16      1008             2|13         low Small-Large isolated
## 1452          4  16      1008             2|13         low Small-Large isolated
## 1453          4  16      1008             2|13         low Small-Large isolated
## 1454          4  16      1008             2|13         low Small-Large isolated
## 1455          4  16      1008             2|13         low Small-Large isolated
## 1456          4  16      1008             2|13         low Small-Large isolated
## 1457          4  16      1008             2|13         low Small-Large isolated
## 1458          4  16      1008             2|13         low Small-Large isolated
## 1459          4  16      1008             2|13         low Small-Large isolated
## 1460          4  16      1008             2|13         low Small-Large isolated
## 1461          4  16      1008             2|13         low Small-Large isolated
## 1462          4  16      1008             2|13         low Small-Large isolated
## 1463          4  16      1008             2|13         low Small-Large isolated
## 1464          4  16      1008             2|13         low Small-Large isolated
## 1465          5  20      1008             2|13         low Small-Large isolated
## 1466          5  20      1008             2|13         low Small-Large isolated
## 1467          5  20      1008             2|13         low Small-Large isolated
## 1468          5  20      1008             2|13         low Small-Large isolated
## 1469          5  20      1008             2|13         low Small-Large isolated
## 1470          5  20      1008             2|13         low Small-Large isolated
## 1471          5  20      1008             2|13         low Small-Large isolated
## 1472          5  20      1008             2|13         low Small-Large isolated
## 1473          5  20      1008             2|13         low Small-Large isolated
## 1474          5  20      1008             2|13         low Small-Large isolated
## 1475          5  20      1008             2|13         low Small-Large isolated
## 1476          5  20      1008             2|13         low Small-Large isolated
## 1477          5  20      1008             2|13         low Small-Large isolated
## 1478          5  20      1008             2|13         low Small-Large isolated
## 1479          5  20      1008             2|13         low Small-Large isolated
## 1480          5  20      1008             2|13         low Small-Large isolated
## 1481          5  20      1008             2|13         low Small-Large isolated
## 1482          5  20      1008             2|13         low Small-Large isolated
## 1483          5  20      1008             2|13         low Small-Large isolated
## 1484          5  20      1008             2|13         low Small-Large isolated
## 1485          5  20      1008             2|13         low Small-Large isolated
## 1486          5  20      1008             2|13         low Small-Large isolated
## 1487          5  20      1008             2|13         low Small-Large isolated
## 1488          5  20      1008             2|13         low Small-Large isolated
## 1489          6  24      1008             2|13         low Small-Large isolated
## 1490          6  24      1008             2|13         low Small-Large isolated
## 1491          6  24      1008             2|13         low Small-Large isolated
## 1492          6  24      1008             2|13         low Small-Large isolated
## 1493          6  24      1008             2|13         low Small-Large isolated
## 1494          6  24      1008             2|13         low Small-Large isolated
## 1495          6  24      1008             2|13         low Small-Large isolated
## 1496          6  24      1008             2|13         low Small-Large isolated
## 1497          6  24      1008             2|13         low Small-Large isolated
## 1498          6  24      1008             2|13         low Small-Large isolated
## 1499          6  24      1008             2|13         low Small-Large isolated
## 1500          6  24      1008             2|13         low Small-Large isolated
## 1501          6  24      1008             2|13         low Small-Large isolated
## 1502          6  24      1008             2|13         low Small-Large isolated
## 1503          6  24      1008             2|13         low Small-Large isolated
## 1504          6  24      1008             2|13         low Small-Large isolated
## 1505          6  24      1008             2|13         low Small-Large isolated
## 1506          6  24      1008             2|13         low Small-Large isolated
## 1507          6  24      1008             2|13         low Small-Large isolated
## 1508          6  24      1008             2|13         low Small-Large isolated
## 1509          6  24      1008             2|13         low Small-Large isolated
## 1510          6  24      1008             2|13         low Small-Large isolated
## 1511          6  24      1008             2|13         low Small-Large isolated
## 1512          6  24      1008             2|13         low Small-Large isolated
## 1513          7  28      1008             2|13         low Small-Large isolated
## 1514          7  28      1008             2|13         low Small-Large isolated
## 1515          7  28      1008             2|13         low Small-Large isolated
## 1516          7  28      1008             2|13         low Small-Large isolated
## 1517          7  28      1008             2|13         low Small-Large isolated
## 1518          7  28      1008             2|13         low Small-Large isolated
## 1519          7  28      1008             2|13         low Small-Large isolated
## 1520          7  28      1008             2|13         low Small-Large isolated
## 1521          7  28      1008             2|13         low Small-Large isolated
## 1522          7  28      1008             2|13         low Small-Large isolated
## 1523          7  28      1008             2|13         low Small-Large isolated
## 1524          7  28      1008             2|13         low Small-Large isolated
## 1525          7  28      1008             2|13         low Small-Large isolated
## 1526          7  28      1008             2|13         low Small-Large isolated
## 1527          7  28      1008             2|13         low Small-Large isolated
## 1528          7  28      1008             2|13         low Small-Large isolated
## 1529          7  28      1008             2|13         low Small-Large isolated
## 1530          7  28      1008             2|13         low Small-Large isolated
## 1531          7  28      1008             2|13         low Small-Large isolated
## 1532          7  28      1008             2|13         low Small-Large isolated
## 1533          7  28      1008             2|13         low Small-Large isolated
## 1534          7  28      1008             2|13         low Small-Large isolated
## 1535          7  28      1008             2|13         low Small-Large isolated
## 1536          7  28      1008             2|13         low Small-Large isolated
## 1537          0   0      1009             2|14         low Small-Large isolated
## 1538          0   0      1009             2|14         low Small-Large isolated
## 1539          0   0      1009             2|14         low Small-Large isolated
## 1540          0   0      1009             2|14         low Small-Large isolated
## 1541          0   0      1009             2|14         low Small-Large isolated
## 1542          0   0      1009             2|14         low Small-Large isolated
## 1543          0   0      1009             2|14         low Small-Large isolated
## 1544          0   0      1009             2|14         low Small-Large isolated
## 1545          0   0      1009             2|14         low Small-Large isolated
## 1546          0   0      1009             2|14         low Small-Large isolated
## 1547          0   0      1009             2|14         low Small-Large isolated
## 1548          0   0      1009             2|14         low Small-Large isolated
## 1549          0   0      1009             2|14         low Small-Large isolated
## 1550          0   0      1009             2|14         low Small-Large isolated
## 1551          0   0      1009             2|14         low Small-Large isolated
## 1552          0   0      1009             2|14         low Small-Large isolated
## 1553          0   0      1009             2|14         low Small-Large isolated
## 1554          0   0      1009             2|14         low Small-Large isolated
## 1555          0   0      1009             2|14         low Small-Large isolated
## 1556          0   0      1009             2|14         low Small-Large isolated
## 1557          0   0      1009             2|14         low Small-Large isolated
## 1558          0   0      1009             2|14         low Small-Large isolated
## 1559          0   0      1009             2|14         low Small-Large isolated
## 1560          0   0      1009             2|14         low Small-Large isolated
## 1561          1   4      1009             2|14         low Small-Large isolated
## 1562          1   4      1009             2|14         low Small-Large isolated
## 1563          1   4      1009             2|14         low Small-Large isolated
## 1564          1   4      1009             2|14         low Small-Large isolated
## 1565          1   4      1009             2|14         low Small-Large isolated
## 1566          1   4      1009             2|14         low Small-Large isolated
## 1567          1   4      1009             2|14         low Small-Large isolated
## 1568          1   4      1009             2|14         low Small-Large isolated
## 1569          1   4      1009             2|14         low Small-Large isolated
## 1570          1   4      1009             2|14         low Small-Large isolated
## 1571          1   4      1009             2|14         low Small-Large isolated
## 1572          1   4      1009             2|14         low Small-Large isolated
## 1573          1   4      1009             2|14         low Small-Large isolated
## 1574          1   4      1009             2|14         low Small-Large isolated
## 1575          1   4      1009             2|14         low Small-Large isolated
## 1576          1   4      1009             2|14         low Small-Large isolated
## 1577          1   4      1009             2|14         low Small-Large isolated
## 1578          1   4      1009             2|14         low Small-Large isolated
## 1579          1   4      1009             2|14         low Small-Large isolated
## 1580          1   4      1009             2|14         low Small-Large isolated
## 1581          1   4      1009             2|14         low Small-Large isolated
## 1582          1   4      1009             2|14         low Small-Large isolated
## 1583          1   4      1009             2|14         low Small-Large isolated
## 1584          1   4      1009             2|14         low Small-Large isolated
## 1585          2   8      1009             2|14         low Small-Large isolated
## 1586          2   8      1009             2|14         low Small-Large isolated
## 1587          2   8      1009             2|14         low Small-Large isolated
## 1588          2   8      1009             2|14         low Small-Large isolated
## 1589          2   8      1009             2|14         low Small-Large isolated
## 1590          2   8      1009             2|14         low Small-Large isolated
## 1591          2   8      1009             2|14         low Small-Large isolated
## 1592          2   8      1009             2|14         low Small-Large isolated
## 1593          2   8      1009             2|14         low Small-Large isolated
## 1594          2   8      1009             2|14         low Small-Large isolated
## 1595          2   8      1009             2|14         low Small-Large isolated
## 1596          2   8      1009             2|14         low Small-Large isolated
## 1597          2   8      1009             2|14         low Small-Large isolated
## 1598          2   8      1009             2|14         low Small-Large isolated
## 1599          2   8      1009             2|14         low Small-Large isolated
## 1600          2   8      1009             2|14         low Small-Large isolated
## 1601          2   8      1009             2|14         low Small-Large isolated
## 1602          2   8      1009             2|14         low Small-Large isolated
## 1603          2   8      1009             2|14         low Small-Large isolated
## 1604          2   8      1009             2|14         low Small-Large isolated
## 1605          2   8      1009             2|14         low Small-Large isolated
## 1606          2   8      1009             2|14         low Small-Large isolated
## 1607          2   8      1009             2|14         low Small-Large isolated
## 1608          2   8      1009             2|14         low Small-Large isolated
## 1609          3  12      1009             2|14         low Small-Large isolated
## 1610          3  12      1009             2|14         low Small-Large isolated
## 1611          3  12      1009             2|14         low Small-Large isolated
## 1612          3  12      1009             2|14         low Small-Large isolated
## 1613          3  12      1009             2|14         low Small-Large isolated
## 1614          3  12      1009             2|14         low Small-Large isolated
## 1615          3  12      1009             2|14         low Small-Large isolated
## 1616          3  12      1009             2|14         low Small-Large isolated
## 1617          3  12      1009             2|14         low Small-Large isolated
## 1618          3  12      1009             2|14         low Small-Large isolated
## 1619          3  12      1009             2|14         low Small-Large isolated
## 1620          3  12      1009             2|14         low Small-Large isolated
## 1621          3  12      1009             2|14         low Small-Large isolated
## 1622          3  12      1009             2|14         low Small-Large isolated
## 1623          3  12      1009             2|14         low Small-Large isolated
## 1624          3  12      1009             2|14         low Small-Large isolated
## 1625          3  12      1009             2|14         low Small-Large isolated
## 1626          3  12      1009             2|14         low Small-Large isolated
## 1627          3  12      1009             2|14         low Small-Large isolated
## 1628          3  12      1009             2|14         low Small-Large isolated
## 1629          3  12      1009             2|14         low Small-Large isolated
## 1630          3  12      1009             2|14         low Small-Large isolated
## 1631          3  12      1009             2|14         low Small-Large isolated
## 1632          3  12      1009             2|14         low Small-Large isolated
## 1633          4  16      1009             2|14         low Small-Large isolated
## 1634          4  16      1009             2|14         low Small-Large isolated
## 1635          4  16      1009             2|14         low Small-Large isolated
## 1636          4  16      1009             2|14         low Small-Large isolated
## 1637          4  16      1009             2|14         low Small-Large isolated
## 1638          4  16      1009             2|14         low Small-Large isolated
## 1639          4  16      1009             2|14         low Small-Large isolated
## 1640          4  16      1009             2|14         low Small-Large isolated
## 1641          4  16      1009             2|14         low Small-Large isolated
## 1642          4  16      1009             2|14         low Small-Large isolated
## 1643          4  16      1009             2|14         low Small-Large isolated
## 1644          4  16      1009             2|14         low Small-Large isolated
## 1645          4  16      1009             2|14         low Small-Large isolated
## 1646          4  16      1009             2|14         low Small-Large isolated
## 1647          4  16      1009             2|14         low Small-Large isolated
## 1648          4  16      1009             2|14         low Small-Large isolated
## 1649          4  16      1009             2|14         low Small-Large isolated
## 1650          4  16      1009             2|14         low Small-Large isolated
## 1651          4  16      1009             2|14         low Small-Large isolated
## 1652          4  16      1009             2|14         low Small-Large isolated
## 1653          4  16      1009             2|14         low Small-Large isolated
## 1654          4  16      1009             2|14         low Small-Large isolated
## 1655          4  16      1009             2|14         low Small-Large isolated
## 1656          4  16      1009             2|14         low Small-Large isolated
## 1657          5  20      1009             2|14         low Small-Large isolated
## 1658          5  20      1009             2|14         low Small-Large isolated
## 1659          5  20      1009             2|14         low Small-Large isolated
## 1660          5  20      1009             2|14         low Small-Large isolated
## 1661          5  20      1009             2|14         low Small-Large isolated
## 1662          5  20      1009             2|14         low Small-Large isolated
## 1663          5  20      1009             2|14         low Small-Large isolated
## 1664          5  20      1009             2|14         low Small-Large isolated
## 1665          5  20      1009             2|14         low Small-Large isolated
## 1666          5  20      1009             2|14         low Small-Large isolated
## 1667          5  20      1009             2|14         low Small-Large isolated
## 1668          5  20      1009             2|14         low Small-Large isolated
## 1669          5  20      1009             2|14         low Small-Large isolated
## 1670          5  20      1009             2|14         low Small-Large isolated
## 1671          5  20      1009             2|14         low Small-Large isolated
## 1672          5  20      1009             2|14         low Small-Large isolated
## 1673          5  20      1009             2|14         low Small-Large isolated
## 1674          5  20      1009             2|14         low Small-Large isolated
## 1675          5  20      1009             2|14         low Small-Large isolated
## 1676          5  20      1009             2|14         low Small-Large isolated
## 1677          5  20      1009             2|14         low Small-Large isolated
## 1678          5  20      1009             2|14         low Small-Large isolated
## 1679          5  20      1009             2|14         low Small-Large isolated
## 1680          5  20      1009             2|14         low Small-Large isolated
## 1681          6  24      1009             2|14         low Small-Large isolated
## 1682          6  24      1009             2|14         low Small-Large isolated
## 1683          6  24      1009             2|14         low Small-Large isolated
## 1684          6  24      1009             2|14         low Small-Large isolated
## 1685          6  24      1009             2|14         low Small-Large isolated
## 1686          6  24      1009             2|14         low Small-Large isolated
## 1687          6  24      1009             2|14         low Small-Large isolated
## 1688          6  24      1009             2|14         low Small-Large isolated
## 1689          6  24      1009             2|14         low Small-Large isolated
## 1690          6  24      1009             2|14         low Small-Large isolated
## 1691          6  24      1009             2|14         low Small-Large isolated
## 1692          6  24      1009             2|14         low Small-Large isolated
## 1693          6  24      1009             2|14         low Small-Large isolated
## 1694          6  24      1009             2|14         low Small-Large isolated
## 1695          6  24      1009             2|14         low Small-Large isolated
## 1696          6  24      1009             2|14         low Small-Large isolated
## 1697          6  24      1009             2|14         low Small-Large isolated
## 1698          6  24      1009             2|14         low Small-Large isolated
## 1699          6  24      1009             2|14         low Small-Large isolated
## 1700          6  24      1009             2|14         low Small-Large isolated
## 1701          6  24      1009             2|14         low Small-Large isolated
## 1702          6  24      1009             2|14         low Small-Large isolated
## 1703          6  24      1009             2|14         low Small-Large isolated
## 1704          6  24      1009             2|14         low Small-Large isolated
## 1705          7  28      1009             2|14         low Small-Large isolated
## 1706          7  28      1009             2|14         low Small-Large isolated
## 1707          7  28      1009             2|14         low Small-Large isolated
## 1708          7  28      1009             2|14         low Small-Large isolated
## 1709          7  28      1009             2|14         low Small-Large isolated
## 1710          7  28      1009             2|14         low Small-Large isolated
## 1711          7  28      1009             2|14         low Small-Large isolated
## 1712          7  28      1009             2|14         low Small-Large isolated
## 1713          7  28      1009             2|14         low Small-Large isolated
## 1714          7  28      1009             2|14         low Small-Large isolated
## 1715          7  28      1009             2|14         low Small-Large isolated
## 1716          7  28      1009             2|14         low Small-Large isolated
## 1717          7  28      1009             2|14         low Small-Large isolated
## 1718          7  28      1009             2|14         low Small-Large isolated
## 1719          7  28      1009             2|14         low Small-Large isolated
## 1720          7  28      1009             2|14         low Small-Large isolated
## 1721          7  28      1009             2|14         low Small-Large isolated
## 1722          7  28      1009             2|14         low Small-Large isolated
## 1723          7  28      1009             2|14         low Small-Large isolated
## 1724          7  28      1009             2|14         low Small-Large isolated
## 1725          7  28      1009             2|14         low Small-Large isolated
## 1726          7  28      1009             2|14         low Small-Large isolated
## 1727          7  28      1009             2|14         low Small-Large isolated
## 1728          7  28      1009             2|14         low Small-Large isolated
## 1729          0   0      1010             2|15         low Small-Large isolated
## 1730          0   0      1010             2|15         low Small-Large isolated
## 1731          0   0      1010             2|15         low Small-Large isolated
## 1732          0   0      1010             2|15         low Small-Large isolated
## 1733          0   0      1010             2|15         low Small-Large isolated
## 1734          0   0      1010             2|15         low Small-Large isolated
## 1735          0   0      1010             2|15         low Small-Large isolated
## 1736          0   0      1010             2|15         low Small-Large isolated
## 1737          0   0      1010             2|15         low Small-Large isolated
## 1738          0   0      1010             2|15         low Small-Large isolated
## 1739          0   0      1010             2|15         low Small-Large isolated
## 1740          0   0      1010             2|15         low Small-Large isolated
## 1741          0   0      1010             2|15         low Small-Large isolated
## 1742          0   0      1010             2|15         low Small-Large isolated
## 1743          0   0      1010             2|15         low Small-Large isolated
## 1744          0   0      1010             2|15         low Small-Large isolated
## 1745          0   0      1010             2|15         low Small-Large isolated
## 1746          0   0      1010             2|15         low Small-Large isolated
## 1747          0   0      1010             2|15         low Small-Large isolated
## 1748          0   0      1010             2|15         low Small-Large isolated
## 1749          0   0      1010             2|15         low Small-Large isolated
## 1750          0   0      1010             2|15         low Small-Large isolated
## 1751          0   0      1010             2|15         low Small-Large isolated
## 1752          0   0      1010             2|15         low Small-Large isolated
## 1753          1   4      1010             2|15         low Small-Large isolated
## 1754          1   4      1010             2|15         low Small-Large isolated
## 1755          1   4      1010             2|15         low Small-Large isolated
## 1756          1   4      1010             2|15         low Small-Large isolated
## 1757          1   4      1010             2|15         low Small-Large isolated
## 1758          1   4      1010             2|15         low Small-Large isolated
## 1759          1   4      1010             2|15         low Small-Large isolated
## 1760          1   4      1010             2|15         low Small-Large isolated
## 1761          1   4      1010             2|15         low Small-Large isolated
## 1762          1   4      1010             2|15         low Small-Large isolated
## 1763          1   4      1010             2|15         low Small-Large isolated
## 1764          1   4      1010             2|15         low Small-Large isolated
## 1765          1   4      1010             2|15         low Small-Large isolated
## 1766          1   4      1010             2|15         low Small-Large isolated
## 1767          1   4      1010             2|15         low Small-Large isolated
## 1768          1   4      1010             2|15         low Small-Large isolated
## 1769          1   4      1010             2|15         low Small-Large isolated
## 1770          1   4      1010             2|15         low Small-Large isolated
## 1771          1   4      1010             2|15         low Small-Large isolated
## 1772          1   4      1010             2|15         low Small-Large isolated
## 1773          1   4      1010             2|15         low Small-Large isolated
## 1774          1   4      1010             2|15         low Small-Large isolated
## 1775          1   4      1010             2|15         low Small-Large isolated
## 1776          1   4      1010             2|15         low Small-Large isolated
## 1777          2   8      1010             2|15         low Small-Large isolated
## 1778          2   8      1010             2|15         low Small-Large isolated
## 1779          2   8      1010             2|15         low Small-Large isolated
## 1780          2   8      1010             2|15         low Small-Large isolated
## 1781          2   8      1010             2|15         low Small-Large isolated
## 1782          2   8      1010             2|15         low Small-Large isolated
## 1783          2   8      1010             2|15         low Small-Large isolated
## 1784          2   8      1010             2|15         low Small-Large isolated
## 1785          2   8      1010             2|15         low Small-Large isolated
## 1786          2   8      1010             2|15         low Small-Large isolated
## 1787          2   8      1010             2|15         low Small-Large isolated
## 1788          2   8      1010             2|15         low Small-Large isolated
## 1789          2   8      1010             2|15         low Small-Large isolated
## 1790          2   8      1010             2|15         low Small-Large isolated
## 1791          2   8      1010             2|15         low Small-Large isolated
## 1792          2   8      1010             2|15         low Small-Large isolated
## 1793          2   8      1010             2|15         low Small-Large isolated
## 1794          2   8      1010             2|15         low Small-Large isolated
## 1795          2   8      1010             2|15         low Small-Large isolated
## 1796          2   8      1010             2|15         low Small-Large isolated
## 1797          2   8      1010             2|15         low Small-Large isolated
## 1798          2   8      1010             2|15         low Small-Large isolated
## 1799          2   8      1010             2|15         low Small-Large isolated
## 1800          2   8      1010             2|15         low Small-Large isolated
## 1801          3  12      1010             2|15         low Small-Large isolated
## 1802          3  12      1010             2|15         low Small-Large isolated
## 1803          3  12      1010             2|15         low Small-Large isolated
## 1804          3  12      1010             2|15         low Small-Large isolated
## 1805          3  12      1010             2|15         low Small-Large isolated
## 1806          3  12      1010             2|15         low Small-Large isolated
## 1807          3  12      1010             2|15         low Small-Large isolated
## 1808          3  12      1010             2|15         low Small-Large isolated
## 1809          3  12      1010             2|15         low Small-Large isolated
## 1810          3  12      1010             2|15         low Small-Large isolated
## 1811          3  12      1010             2|15         low Small-Large isolated
## 1812          3  12      1010             2|15         low Small-Large isolated
## 1813          3  12      1010             2|15         low Small-Large isolated
## 1814          3  12      1010             2|15         low Small-Large isolated
## 1815          3  12      1010             2|15         low Small-Large isolated
## 1816          3  12      1010             2|15         low Small-Large isolated
## 1817          3  12      1010             2|15         low Small-Large isolated
## 1818          3  12      1010             2|15         low Small-Large isolated
## 1819          3  12      1010             2|15         low Small-Large isolated
## 1820          3  12      1010             2|15         low Small-Large isolated
## 1821          3  12      1010             2|15         low Small-Large isolated
## 1822          3  12      1010             2|15         low Small-Large isolated
## 1823          3  12      1010             2|15         low Small-Large isolated
## 1824          3  12      1010             2|15         low Small-Large isolated
## 1825          4  16      1010             2|15         low Small-Large isolated
## 1826          4  16      1010             2|15         low Small-Large isolated
## 1827          4  16      1010             2|15         low Small-Large isolated
## 1828          4  16      1010             2|15         low Small-Large isolated
## 1829          4  16      1010             2|15         low Small-Large isolated
## 1830          4  16      1010             2|15         low Small-Large isolated
## 1831          4  16      1010             2|15         low Small-Large isolated
## 1832          4  16      1010             2|15         low Small-Large isolated
## 1833          4  16      1010             2|15         low Small-Large isolated
## 1834          4  16      1010             2|15         low Small-Large isolated
## 1835          4  16      1010             2|15         low Small-Large isolated
## 1836          4  16      1010             2|15         low Small-Large isolated
## 1837          4  16      1010             2|15         low Small-Large isolated
## 1838          4  16      1010             2|15         low Small-Large isolated
## 1839          4  16      1010             2|15         low Small-Large isolated
## 1840          4  16      1010             2|15         low Small-Large isolated
## 1841          4  16      1010             2|15         low Small-Large isolated
## 1842          4  16      1010             2|15         low Small-Large isolated
## 1843          4  16      1010             2|15         low Small-Large isolated
## 1844          4  16      1010             2|15         low Small-Large isolated
## 1845          4  16      1010             2|15         low Small-Large isolated
## 1846          4  16      1010             2|15         low Small-Large isolated
## 1847          4  16      1010             2|15         low Small-Large isolated
## 1848          4  16      1010             2|15         low Small-Large isolated
## 1849          5  20      1010             2|15         low Small-Large isolated
## 1850          5  20      1010             2|15         low Small-Large isolated
## 1851          5  20      1010             2|15         low Small-Large isolated
## 1852          5  20      1010             2|15         low Small-Large isolated
## 1853          5  20      1010             2|15         low Small-Large isolated
## 1854          5  20      1010             2|15         low Small-Large isolated
## 1855          5  20      1010             2|15         low Small-Large isolated
## 1856          5  20      1010             2|15         low Small-Large isolated
## 1857          5  20      1010             2|15         low Small-Large isolated
## 1858          5  20      1010             2|15         low Small-Large isolated
## 1859          5  20      1010             2|15         low Small-Large isolated
## 1860          5  20      1010             2|15         low Small-Large isolated
## 1861          5  20      1010             2|15         low Small-Large isolated
## 1862          5  20      1010             2|15         low Small-Large isolated
## 1863          5  20      1010             2|15         low Small-Large isolated
## 1864          5  20      1010             2|15         low Small-Large isolated
## 1865          5  20      1010             2|15         low Small-Large isolated
## 1866          5  20      1010             2|15         low Small-Large isolated
## 1867          5  20      1010             2|15         low Small-Large isolated
## 1868          5  20      1010             2|15         low Small-Large isolated
## 1869          5  20      1010             2|15         low Small-Large isolated
## 1870          5  20      1010             2|15         low Small-Large isolated
## 1871          5  20      1010             2|15         low Small-Large isolated
## 1872          5  20      1010             2|15         low Small-Large isolated
## 1873          6  24      1010             2|15         low Small-Large isolated
## 1874          6  24      1010             2|15         low Small-Large isolated
## 1875          6  24      1010             2|15         low Small-Large isolated
## 1876          6  24      1010             2|15         low Small-Large isolated
## 1877          6  24      1010             2|15         low Small-Large isolated
## 1878          6  24      1010             2|15         low Small-Large isolated
## 1879          6  24      1010             2|15         low Small-Large isolated
## 1880          6  24      1010             2|15         low Small-Large isolated
## 1881          6  24      1010             2|15         low Small-Large isolated
## 1882          6  24      1010             2|15         low Small-Large isolated
## 1883          6  24      1010             2|15         low Small-Large isolated
## 1884          6  24      1010             2|15         low Small-Large isolated
## 1885          6  24      1010             2|15         low Small-Large isolated
## 1886          6  24      1010             2|15         low Small-Large isolated
## 1887          6  24      1010             2|15         low Small-Large isolated
## 1888          6  24      1010             2|15         low Small-Large isolated
## 1889          6  24      1010             2|15         low Small-Large isolated
## 1890          6  24      1010             2|15         low Small-Large isolated
## 1891          6  24      1010             2|15         low Small-Large isolated
## 1892          6  24      1010             2|15         low Small-Large isolated
## 1893          6  24      1010             2|15         low Small-Large isolated
## 1894          6  24      1010             2|15         low Small-Large isolated
## 1895          6  24      1010             2|15         low Small-Large isolated
## 1896          6  24      1010             2|15         low Small-Large isolated
## 1897          7  28      1010             2|15         low Small-Large isolated
## 1898          7  28      1010             2|15         low Small-Large isolated
## 1899          7  28      1010             2|15         low Small-Large isolated
## 1900          7  28      1010             2|15         low Small-Large isolated
## 1901          7  28      1010             2|15         low Small-Large isolated
## 1902          7  28      1010             2|15         low Small-Large isolated
## 1903          7  28      1010             2|15         low Small-Large isolated
## 1904          7  28      1010             2|15         low Small-Large isolated
## 1905          7  28      1010             2|15         low Small-Large isolated
## 1906          7  28      1010             2|15         low Small-Large isolated
## 1907          7  28      1010             2|15         low Small-Large isolated
## 1908          7  28      1010             2|15         low Small-Large isolated
## 1909          7  28      1010             2|15         low Small-Large isolated
## 1910          7  28      1010             2|15         low Small-Large isolated
## 1911          7  28      1010             2|15         low Small-Large isolated
## 1912          7  28      1010             2|15         low Small-Large isolated
## 1913          7  28      1010             2|15         low Small-Large isolated
## 1914          7  28      1010             2|15         low Small-Large isolated
## 1915          7  28      1010             2|15         low Small-Large isolated
## 1916          7  28      1010             2|15         low Small-Large isolated
## 1917          7  28      1010             2|15         low Small-Large isolated
## 1918          7  28      1010             2|15         low Small-Large isolated
## 1919          7  28      1010             2|15         low Small-Large isolated
## 1920          7  28      1010             2|15         low Small-Large isolated
## 1921          0   0      1011             3|11         low Small-Large isolated
## 1922          0   0      1011             3|11         low Small-Large isolated
## 1923          0   0      1011             3|11         low Small-Large isolated
## 1924          0   0      1011             3|11         low Small-Large isolated
## 1925          0   0      1011             3|11         low Small-Large isolated
## 1926          0   0      1011             3|11         low Small-Large isolated
## 1927          0   0      1011             3|11         low Small-Large isolated
## 1928          0   0      1011             3|11         low Small-Large isolated
## 1929          0   0      1011             3|11         low Small-Large isolated
## 1930          0   0      1011             3|11         low Small-Large isolated
## 1931          0   0      1011             3|11         low Small-Large isolated
## 1932          0   0      1011             3|11         low Small-Large isolated
## 1933          0   0      1011             3|11         low Small-Large isolated
## 1934          0   0      1011             3|11         low Small-Large isolated
## 1935          0   0      1011             3|11         low Small-Large isolated
## 1936          0   0      1011             3|11         low Small-Large isolated
## 1937          0   0      1011             3|11         low Small-Large isolated
## 1938          0   0      1011             3|11         low Small-Large isolated
## 1939          0   0      1011             3|11         low Small-Large isolated
## 1940          0   0      1011             3|11         low Small-Large isolated
## 1941          0   0      1011             3|11         low Small-Large isolated
## 1942          0   0      1011             3|11         low Small-Large isolated
## 1943          0   0      1011             3|11         low Small-Large isolated
## 1944          0   0      1011             3|11         low Small-Large isolated
## 1945          1   4      1011             3|11         low Small-Large isolated
## 1946          1   4      1011             3|11         low Small-Large isolated
## 1947          1   4      1011             3|11         low Small-Large isolated
## 1948          1   4      1011             3|11         low Small-Large isolated
## 1949          1   4      1011             3|11         low Small-Large isolated
## 1950          1   4      1011             3|11         low Small-Large isolated
## 1951          1   4      1011             3|11         low Small-Large isolated
## 1952          1   4      1011             3|11         low Small-Large isolated
## 1953          1   4      1011             3|11         low Small-Large isolated
## 1954          1   4      1011             3|11         low Small-Large isolated
## 1955          1   4      1011             3|11         low Small-Large isolated
## 1956          1   4      1011             3|11         low Small-Large isolated
## 1957          1   4      1011             3|11         low Small-Large isolated
## 1958          1   4      1011             3|11         low Small-Large isolated
## 1959          1   4      1011             3|11         low Small-Large isolated
## 1960          1   4      1011             3|11         low Small-Large isolated
## 1961          1   4      1011             3|11         low Small-Large isolated
## 1962          1   4      1011             3|11         low Small-Large isolated
## 1963          1   4      1011             3|11         low Small-Large isolated
## 1964          1   4      1011             3|11         low Small-Large isolated
## 1965          1   4      1011             3|11         low Small-Large isolated
## 1966          1   4      1011             3|11         low Small-Large isolated
## 1967          1   4      1011             3|11         low Small-Large isolated
## 1968          1   4      1011             3|11         low Small-Large isolated
## 1969          2   8      1011             3|11         low Small-Large isolated
## 1970          2   8      1011             3|11         low Small-Large isolated
## 1971          2   8      1011             3|11         low Small-Large isolated
## 1972          2   8      1011             3|11         low Small-Large isolated
## 1973          2   8      1011             3|11         low Small-Large isolated
## 1974          2   8      1011             3|11         low Small-Large isolated
## 1975          2   8      1011             3|11         low Small-Large isolated
## 1976          2   8      1011             3|11         low Small-Large isolated
## 1977          2   8      1011             3|11         low Small-Large isolated
## 1978          2   8      1011             3|11         low Small-Large isolated
## 1979          2   8      1011             3|11         low Small-Large isolated
## 1980          2   8      1011             3|11         low Small-Large isolated
## 1981          2   8      1011             3|11         low Small-Large isolated
## 1982          2   8      1011             3|11         low Small-Large isolated
## 1983          2   8      1011             3|11         low Small-Large isolated
## 1984          2   8      1011             3|11         low Small-Large isolated
## 1985          2   8      1011             3|11         low Small-Large isolated
## 1986          2   8      1011             3|11         low Small-Large isolated
## 1987          2   8      1011             3|11         low Small-Large isolated
## 1988          2   8      1011             3|11         low Small-Large isolated
## 1989          2   8      1011             3|11         low Small-Large isolated
## 1990          2   8      1011             3|11         low Small-Large isolated
## 1991          2   8      1011             3|11         low Small-Large isolated
## 1992          2   8      1011             3|11         low Small-Large isolated
## 1993          3  12      1011             3|11         low Small-Large isolated
## 1994          3  12      1011             3|11         low Small-Large isolated
## 1995          3  12      1011             3|11         low Small-Large isolated
## 1996          3  12      1011             3|11         low Small-Large isolated
## 1997          3  12      1011             3|11         low Small-Large isolated
## 1998          3  12      1011             3|11         low Small-Large isolated
## 1999          3  12      1011             3|11         low Small-Large isolated
## 2000          3  12      1011             3|11         low Small-Large isolated
## 2001          3  12      1011             3|11         low Small-Large isolated
## 2002          3  12      1011             3|11         low Small-Large isolated
## 2003          3  12      1011             3|11         low Small-Large isolated
## 2004          3  12      1011             3|11         low Small-Large isolated
## 2005          3  12      1011             3|11         low Small-Large isolated
## 2006          3  12      1011             3|11         low Small-Large isolated
## 2007          3  12      1011             3|11         low Small-Large isolated
## 2008          3  12      1011             3|11         low Small-Large isolated
## 2009          3  12      1011             3|11         low Small-Large isolated
## 2010          3  12      1011             3|11         low Small-Large isolated
## 2011          3  12      1011             3|11         low Small-Large isolated
## 2012          3  12      1011             3|11         low Small-Large isolated
## 2013          3  12      1011             3|11         low Small-Large isolated
## 2014          3  12      1011             3|11         low Small-Large isolated
## 2015          3  12      1011             3|11         low Small-Large isolated
## 2016          3  12      1011             3|11         low Small-Large isolated
## 2017          4  16      1011             3|11         low Small-Large isolated
## 2018          4  16      1011             3|11         low Small-Large isolated
## 2019          4  16      1011             3|11         low Small-Large isolated
## 2020          4  16      1011             3|11         low Small-Large isolated
## 2021          4  16      1011             3|11         low Small-Large isolated
## 2022          4  16      1011             3|11         low Small-Large isolated
## 2023          4  16      1011             3|11         low Small-Large isolated
## 2024          4  16      1011             3|11         low Small-Large isolated
## 2025          4  16      1011             3|11         low Small-Large isolated
## 2026          4  16      1011             3|11         low Small-Large isolated
## 2027          4  16      1011             3|11         low Small-Large isolated
## 2028          4  16      1011             3|11         low Small-Large isolated
## 2029          4  16      1011             3|11         low Small-Large isolated
## 2030          4  16      1011             3|11         low Small-Large isolated
## 2031          4  16      1011             3|11         low Small-Large isolated
## 2032          4  16      1011             3|11         low Small-Large isolated
## 2033          4  16      1011             3|11         low Small-Large isolated
## 2034          4  16      1011             3|11         low Small-Large isolated
## 2035          4  16      1011             3|11         low Small-Large isolated
## 2036          4  16      1011             3|11         low Small-Large isolated
## 2037          4  16      1011             3|11         low Small-Large isolated
## 2038          4  16      1011             3|11         low Small-Large isolated
## 2039          4  16      1011             3|11         low Small-Large isolated
## 2040          4  16      1011             3|11         low Small-Large isolated
## 2041          5  20      1011             3|11         low Small-Large isolated
## 2042          5  20      1011             3|11         low Small-Large isolated
## 2043          5  20      1011             3|11         low Small-Large isolated
## 2044          5  20      1011             3|11         low Small-Large isolated
## 2045          5  20      1011             3|11         low Small-Large isolated
## 2046          5  20      1011             3|11         low Small-Large isolated
## 2047          5  20      1011             3|11         low Small-Large isolated
## 2048          5  20      1011             3|11         low Small-Large isolated
## 2049          5  20      1011             3|11         low Small-Large isolated
## 2050          5  20      1011             3|11         low Small-Large isolated
## 2051          5  20      1011             3|11         low Small-Large isolated
## 2052          5  20      1011             3|11         low Small-Large isolated
## 2053          5  20      1011             3|11         low Small-Large isolated
## 2054          5  20      1011             3|11         low Small-Large isolated
## 2055          5  20      1011             3|11         low Small-Large isolated
## 2056          5  20      1011             3|11         low Small-Large isolated
## 2057          5  20      1011             3|11         low Small-Large isolated
## 2058          5  20      1011             3|11         low Small-Large isolated
## 2059          5  20      1011             3|11         low Small-Large isolated
## 2060          5  20      1011             3|11         low Small-Large isolated
## 2061          5  20      1011             3|11         low Small-Large isolated
## 2062          5  20      1011             3|11         low Small-Large isolated
## 2063          5  20      1011             3|11         low Small-Large isolated
## 2064          5  20      1011             3|11         low Small-Large isolated
## 2065          6  24      1011             3|11         low Small-Large isolated
## 2066          6  24      1011             3|11         low Small-Large isolated
## 2067          6  24      1011             3|11         low Small-Large isolated
## 2068          6  24      1011             3|11         low Small-Large isolated
## 2069          6  24      1011             3|11         low Small-Large isolated
## 2070          6  24      1011             3|11         low Small-Large isolated
## 2071          6  24      1011             3|11         low Small-Large isolated
## 2072          6  24      1011             3|11         low Small-Large isolated
## 2073          6  24      1011             3|11         low Small-Large isolated
## 2074          6  24      1011             3|11         low Small-Large isolated
## 2075          6  24      1011             3|11         low Small-Large isolated
## 2076          6  24      1011             3|11         low Small-Large isolated
## 2077          6  24      1011             3|11         low Small-Large isolated
## 2078          6  24      1011             3|11         low Small-Large isolated
## 2079          6  24      1011             3|11         low Small-Large isolated
## 2080          6  24      1011             3|11         low Small-Large isolated
## 2081          6  24      1011             3|11         low Small-Large isolated
## 2082          6  24      1011             3|11         low Small-Large isolated
## 2083          6  24      1011             3|11         low Small-Large isolated
## 2084          6  24      1011             3|11         low Small-Large isolated
## 2085          6  24      1011             3|11         low Small-Large isolated
## 2086          6  24      1011             3|11         low Small-Large isolated
## 2087          6  24      1011             3|11         low Small-Large isolated
## 2088          6  24      1011             3|11         low Small-Large isolated
## 2089          7  28      1011             3|11         low Small-Large isolated
## 2090          7  28      1011             3|11         low Small-Large isolated
## 2091          7  28      1011             3|11         low Small-Large isolated
## 2092          7  28      1011             3|11         low Small-Large isolated
## 2093          7  28      1011             3|11         low Small-Large isolated
## 2094          7  28      1011             3|11         low Small-Large isolated
## 2095          7  28      1011             3|11         low Small-Large isolated
## 2096          7  28      1011             3|11         low Small-Large isolated
## 2097          7  28      1011             3|11         low Small-Large isolated
## 2098          7  28      1011             3|11         low Small-Large isolated
## 2099          7  28      1011             3|11         low Small-Large isolated
## 2100          7  28      1011             3|11         low Small-Large isolated
## 2101          7  28      1011             3|11         low Small-Large isolated
## 2102          7  28      1011             3|11         low Small-Large isolated
## 2103          7  28      1011             3|11         low Small-Large isolated
## 2104          7  28      1011             3|11         low Small-Large isolated
## 2105          7  28      1011             3|11         low Small-Large isolated
## 2106          7  28      1011             3|11         low Small-Large isolated
## 2107          7  28      1011             3|11         low Small-Large isolated
## 2108          7  28      1011             3|11         low Small-Large isolated
## 2109          7  28      1011             3|11         low Small-Large isolated
## 2110          7  28      1011             3|11         low Small-Large isolated
## 2111          7  28      1011             3|11         low Small-Large isolated
## 2112          7  28      1011             3|11         low Small-Large isolated
## 2113          0   0      1012             3|12         low Small-Large isolated
## 2114          0   0      1012             3|12         low Small-Large isolated
## 2115          0   0      1012             3|12         low Small-Large isolated
## 2116          0   0      1012             3|12         low Small-Large isolated
## 2117          0   0      1012             3|12         low Small-Large isolated
## 2118          0   0      1012             3|12         low Small-Large isolated
## 2119          0   0      1012             3|12         low Small-Large isolated
## 2120          0   0      1012             3|12         low Small-Large isolated
## 2121          0   0      1012             3|12         low Small-Large isolated
## 2122          0   0      1012             3|12         low Small-Large isolated
## 2123          0   0      1012             3|12         low Small-Large isolated
## 2124          0   0      1012             3|12         low Small-Large isolated
## 2125          0   0      1012             3|12         low Small-Large isolated
## 2126          0   0      1012             3|12         low Small-Large isolated
## 2127          0   0      1012             3|12         low Small-Large isolated
## 2128          0   0      1012             3|12         low Small-Large isolated
## 2129          0   0      1012             3|12         low Small-Large isolated
## 2130          0   0      1012             3|12         low Small-Large isolated
## 2131          0   0      1012             3|12         low Small-Large isolated
## 2132          0   0      1012             3|12         low Small-Large isolated
## 2133          0   0      1012             3|12         low Small-Large isolated
## 2134          0   0      1012             3|12         low Small-Large isolated
## 2135          0   0      1012             3|12         low Small-Large isolated
## 2136          0   0      1012             3|12         low Small-Large isolated
## 2137          1   4      1012             3|12         low Small-Large isolated
## 2138          1   4      1012             3|12         low Small-Large isolated
## 2139          1   4      1012             3|12         low Small-Large isolated
## 2140          1   4      1012             3|12         low Small-Large isolated
## 2141          1   4      1012             3|12         low Small-Large isolated
## 2142          1   4      1012             3|12         low Small-Large isolated
## 2143          1   4      1012             3|12         low Small-Large isolated
## 2144          1   4      1012             3|12         low Small-Large isolated
## 2145          1   4      1012             3|12         low Small-Large isolated
## 2146          1   4      1012             3|12         low Small-Large isolated
## 2147          1   4      1012             3|12         low Small-Large isolated
## 2148          1   4      1012             3|12         low Small-Large isolated
## 2149          1   4      1012             3|12         low Small-Large isolated
## 2150          1   4      1012             3|12         low Small-Large isolated
## 2151          1   4      1012             3|12         low Small-Large isolated
## 2152          1   4      1012             3|12         low Small-Large isolated
## 2153          1   4      1012             3|12         low Small-Large isolated
## 2154          1   4      1012             3|12         low Small-Large isolated
## 2155          1   4      1012             3|12         low Small-Large isolated
## 2156          1   4      1012             3|12         low Small-Large isolated
## 2157          1   4      1012             3|12         low Small-Large isolated
## 2158          1   4      1012             3|12         low Small-Large isolated
## 2159          1   4      1012             3|12         low Small-Large isolated
## 2160          1   4      1012             3|12         low Small-Large isolated
## 2161          2   8      1012             3|12         low Small-Large isolated
## 2162          2   8      1012             3|12         low Small-Large isolated
## 2163          2   8      1012             3|12         low Small-Large isolated
## 2164          2   8      1012             3|12         low Small-Large isolated
## 2165          2   8      1012             3|12         low Small-Large isolated
## 2166          2   8      1012             3|12         low Small-Large isolated
## 2167          2   8      1012             3|12         low Small-Large isolated
## 2168          2   8      1012             3|12         low Small-Large isolated
## 2169          2   8      1012             3|12         low Small-Large isolated
## 2170          2   8      1012             3|12         low Small-Large isolated
## 2171          2   8      1012             3|12         low Small-Large isolated
## 2172          2   8      1012             3|12         low Small-Large isolated
## 2173          2   8      1012             3|12         low Small-Large isolated
## 2174          2   8      1012             3|12         low Small-Large isolated
## 2175          2   8      1012             3|12         low Small-Large isolated
## 2176          2   8      1012             3|12         low Small-Large isolated
## 2177          2   8      1012             3|12         low Small-Large isolated
## 2178          2   8      1012             3|12         low Small-Large isolated
## 2179          2   8      1012             3|12         low Small-Large isolated
## 2180          2   8      1012             3|12         low Small-Large isolated
## 2181          2   8      1012             3|12         low Small-Large isolated
## 2182          2   8      1012             3|12         low Small-Large isolated
## 2183          2   8      1012             3|12         low Small-Large isolated
## 2184          2   8      1012             3|12         low Small-Large isolated
## 2185          3  12      1012             3|12         low Small-Large isolated
## 2186          3  12      1012             3|12         low Small-Large isolated
## 2187          3  12      1012             3|12         low Small-Large isolated
## 2188          3  12      1012             3|12         low Small-Large isolated
## 2189          3  12      1012             3|12         low Small-Large isolated
## 2190          3  12      1012             3|12         low Small-Large isolated
## 2191          3  12      1012             3|12         low Small-Large isolated
## 2192          3  12      1012             3|12         low Small-Large isolated
## 2193          3  12      1012             3|12         low Small-Large isolated
## 2194          3  12      1012             3|12         low Small-Large isolated
## 2195          3  12      1012             3|12         low Small-Large isolated
## 2196          3  12      1012             3|12         low Small-Large isolated
## 2197          3  12      1012             3|12         low Small-Large isolated
## 2198          3  12      1012             3|12         low Small-Large isolated
## 2199          3  12      1012             3|12         low Small-Large isolated
## 2200          3  12      1012             3|12         low Small-Large isolated
## 2201          3  12      1012             3|12         low Small-Large isolated
## 2202          3  12      1012             3|12         low Small-Large isolated
## 2203          3  12      1012             3|12         low Small-Large isolated
## 2204          3  12      1012             3|12         low Small-Large isolated
## 2205          3  12      1012             3|12         low Small-Large isolated
## 2206          3  12      1012             3|12         low Small-Large isolated
## 2207          3  12      1012             3|12         low Small-Large isolated
## 2208          3  12      1012             3|12         low Small-Large isolated
## 2209          4  16      1012             3|12         low Small-Large isolated
## 2210          4  16      1012             3|12         low Small-Large isolated
## 2211          4  16      1012             3|12         low Small-Large isolated
## 2212          4  16      1012             3|12         low Small-Large isolated
## 2213          4  16      1012             3|12         low Small-Large isolated
## 2214          4  16      1012             3|12         low Small-Large isolated
## 2215          4  16      1012             3|12         low Small-Large isolated
## 2216          4  16      1012             3|12         low Small-Large isolated
## 2217          4  16      1012             3|12         low Small-Large isolated
## 2218          4  16      1012             3|12         low Small-Large isolated
## 2219          4  16      1012             3|12         low Small-Large isolated
## 2220          4  16      1012             3|12         low Small-Large isolated
## 2221          4  16      1012             3|12         low Small-Large isolated
## 2222          4  16      1012             3|12         low Small-Large isolated
## 2223          4  16      1012             3|12         low Small-Large isolated
## 2224          4  16      1012             3|12         low Small-Large isolated
## 2225          4  16      1012             3|12         low Small-Large isolated
## 2226          4  16      1012             3|12         low Small-Large isolated
## 2227          4  16      1012             3|12         low Small-Large isolated
## 2228          4  16      1012             3|12         low Small-Large isolated
## 2229          4  16      1012             3|12         low Small-Large isolated
## 2230          4  16      1012             3|12         low Small-Large isolated
## 2231          4  16      1012             3|12         low Small-Large isolated
## 2232          4  16      1012             3|12         low Small-Large isolated
## 2233          5  20      1012             3|12         low Small-Large isolated
## 2234          5  20      1012             3|12         low Small-Large isolated
## 2235          5  20      1012             3|12         low Small-Large isolated
## 2236          5  20      1012             3|12         low Small-Large isolated
## 2237          5  20      1012             3|12         low Small-Large isolated
## 2238          5  20      1012             3|12         low Small-Large isolated
## 2239          5  20      1012             3|12         low Small-Large isolated
## 2240          5  20      1012             3|12         low Small-Large isolated
## 2241          5  20      1012             3|12         low Small-Large isolated
## 2242          5  20      1012             3|12         low Small-Large isolated
## 2243          5  20      1012             3|12         low Small-Large isolated
## 2244          5  20      1012             3|12         low Small-Large isolated
## 2245          5  20      1012             3|12         low Small-Large isolated
## 2246          5  20      1012             3|12         low Small-Large isolated
## 2247          5  20      1012             3|12         low Small-Large isolated
## 2248          5  20      1012             3|12         low Small-Large isolated
## 2249          5  20      1012             3|12         low Small-Large isolated
## 2250          5  20      1012             3|12         low Small-Large isolated
## 2251          5  20      1012             3|12         low Small-Large isolated
## 2252          5  20      1012             3|12         low Small-Large isolated
## 2253          5  20      1012             3|12         low Small-Large isolated
## 2254          5  20      1012             3|12         low Small-Large isolated
## 2255          5  20      1012             3|12         low Small-Large isolated
## 2256          5  20      1012             3|12         low Small-Large isolated
## 2257          6  24      1012             3|12         low Small-Large isolated
## 2258          6  24      1012             3|12         low Small-Large isolated
## 2259          6  24      1012             3|12         low Small-Large isolated
## 2260          6  24      1012             3|12         low Small-Large isolated
## 2261          6  24      1012             3|12         low Small-Large isolated
## 2262          6  24      1012             3|12         low Small-Large isolated
## 2263          6  24      1012             3|12         low Small-Large isolated
## 2264          6  24      1012             3|12         low Small-Large isolated
## 2265          6  24      1012             3|12         low Small-Large isolated
## 2266          6  24      1012             3|12         low Small-Large isolated
## 2267          6  24      1012             3|12         low Small-Large isolated
## 2268          6  24      1012             3|12         low Small-Large isolated
## 2269          6  24      1012             3|12         low Small-Large isolated
## 2270          6  24      1012             3|12         low Small-Large isolated
## 2271          6  24      1012             3|12         low Small-Large isolated
## 2272          6  24      1012             3|12         low Small-Large isolated
## 2273          6  24      1012             3|12         low Small-Large isolated
## 2274          6  24      1012             3|12         low Small-Large isolated
## 2275          6  24      1012             3|12         low Small-Large isolated
## 2276          6  24      1012             3|12         low Small-Large isolated
## 2277          6  24      1012             3|12         low Small-Large isolated
## 2278          6  24      1012             3|12         low Small-Large isolated
## 2279          6  24      1012             3|12         low Small-Large isolated
## 2280          6  24      1012             3|12         low Small-Large isolated
## 2281          7  28      1012             3|12         low Small-Large isolated
## 2282          7  28      1012             3|12         low Small-Large isolated
## 2283          7  28      1012             3|12         low Small-Large isolated
## 2284          7  28      1012             3|12         low Small-Large isolated
## 2285          7  28      1012             3|12         low Small-Large isolated
## 2286          7  28      1012             3|12         low Small-Large isolated
## 2287          7  28      1012             3|12         low Small-Large isolated
## 2288          7  28      1012             3|12         low Small-Large isolated
## 2289          7  28      1012             3|12         low Small-Large isolated
## 2290          7  28      1012             3|12         low Small-Large isolated
## 2291          7  28      1012             3|12         low Small-Large isolated
## 2292          7  28      1012             3|12         low Small-Large isolated
## 2293          7  28      1012             3|12         low Small-Large isolated
## 2294          7  28      1012             3|12         low Small-Large isolated
## 2295          7  28      1012             3|12         low Small-Large isolated
## 2296          7  28      1012             3|12         low Small-Large isolated
## 2297          7  28      1012             3|12         low Small-Large isolated
## 2298          7  28      1012             3|12         low Small-Large isolated
## 2299          7  28      1012             3|12         low Small-Large isolated
## 2300          7  28      1012             3|12         low Small-Large isolated
## 2301          7  28      1012             3|12         low Small-Large isolated
## 2302          7  28      1012             3|12         low Small-Large isolated
## 2303          7  28      1012             3|12         low Small-Large isolated
## 2304          7  28      1012             3|12         low Small-Large isolated
## 2305          0   0      1013             3|13         low Small-Large isolated
## 2306          0   0      1013             3|13         low Small-Large isolated
## 2307          0   0      1013             3|13         low Small-Large isolated
## 2308          0   0      1013             3|13         low Small-Large isolated
## 2309          0   0      1013             3|13         low Small-Large isolated
## 2310          0   0      1013             3|13         low Small-Large isolated
## 2311          0   0      1013             3|13         low Small-Large isolated
## 2312          0   0      1013             3|13         low Small-Large isolated
## 2313          0   0      1013             3|13         low Small-Large isolated
## 2314          0   0      1013             3|13         low Small-Large isolated
## 2315          0   0      1013             3|13         low Small-Large isolated
## 2316          0   0      1013             3|13         low Small-Large isolated
## 2317          0   0      1013             3|13         low Small-Large isolated
## 2318          0   0      1013             3|13         low Small-Large isolated
## 2319          0   0      1013             3|13         low Small-Large isolated
## 2320          0   0      1013             3|13         low Small-Large isolated
## 2321          0   0      1013             3|13         low Small-Large isolated
## 2322          0   0      1013             3|13         low Small-Large isolated
## 2323          0   0      1013             3|13         low Small-Large isolated
## 2324          0   0      1013             3|13         low Small-Large isolated
## 2325          0   0      1013             3|13         low Small-Large isolated
## 2326          0   0      1013             3|13         low Small-Large isolated
## 2327          0   0      1013             3|13         low Small-Large isolated
## 2328          0   0      1013             3|13         low Small-Large isolated
## 2329          1   4      1013             3|13         low Small-Large isolated
## 2330          1   4      1013             3|13         low Small-Large isolated
## 2331          1   4      1013             3|13         low Small-Large isolated
## 2332          1   4      1013             3|13         low Small-Large isolated
## 2333          1   4      1013             3|13         low Small-Large isolated
## 2334          1   4      1013             3|13         low Small-Large isolated
## 2335          1   4      1013             3|13         low Small-Large isolated
## 2336          1   4      1013             3|13         low Small-Large isolated
## 2337          1   4      1013             3|13         low Small-Large isolated
## 2338          1   4      1013             3|13         low Small-Large isolated
## 2339          1   4      1013             3|13         low Small-Large isolated
## 2340          1   4      1013             3|13         low Small-Large isolated
## 2341          1   4      1013             3|13         low Small-Large isolated
## 2342          1   4      1013             3|13         low Small-Large isolated
## 2343          1   4      1013             3|13         low Small-Large isolated
## 2344          1   4      1013             3|13         low Small-Large isolated
## 2345          1   4      1013             3|13         low Small-Large isolated
## 2346          1   4      1013             3|13         low Small-Large isolated
## 2347          1   4      1013             3|13         low Small-Large isolated
## 2348          1   4      1013             3|13         low Small-Large isolated
## 2349          1   4      1013             3|13         low Small-Large isolated
## 2350          1   4      1013             3|13         low Small-Large isolated
## 2351          1   4      1013             3|13         low Small-Large isolated
## 2352          1   4      1013             3|13         low Small-Large isolated
## 2353          2   8      1013             3|13         low Small-Large isolated
## 2354          2   8      1013             3|13         low Small-Large isolated
## 2355          2   8      1013             3|13         low Small-Large isolated
## 2356          2   8      1013             3|13         low Small-Large isolated
## 2357          2   8      1013             3|13         low Small-Large isolated
## 2358          2   8      1013             3|13         low Small-Large isolated
## 2359          2   8      1013             3|13         low Small-Large isolated
## 2360          2   8      1013             3|13         low Small-Large isolated
## 2361          2   8      1013             3|13         low Small-Large isolated
## 2362          2   8      1013             3|13         low Small-Large isolated
## 2363          2   8      1013             3|13         low Small-Large isolated
## 2364          2   8      1013             3|13         low Small-Large isolated
## 2365          2   8      1013             3|13         low Small-Large isolated
## 2366          2   8      1013             3|13         low Small-Large isolated
## 2367          2   8      1013             3|13         low Small-Large isolated
## 2368          2   8      1013             3|13         low Small-Large isolated
## 2369          2   8      1013             3|13         low Small-Large isolated
## 2370          2   8      1013             3|13         low Small-Large isolated
## 2371          2   8      1013             3|13         low Small-Large isolated
## 2372          2   8      1013             3|13         low Small-Large isolated
## 2373          2   8      1013             3|13         low Small-Large isolated
## 2374          2   8      1013             3|13         low Small-Large isolated
## 2375          2   8      1013             3|13         low Small-Large isolated
## 2376          2   8      1013             3|13         low Small-Large isolated
## 2377          3  12      1013             3|13         low Small-Large isolated
## 2378          3  12      1013             3|13         low Small-Large isolated
## 2379          3  12      1013             3|13         low Small-Large isolated
## 2380          3  12      1013             3|13         low Small-Large isolated
## 2381          3  12      1013             3|13         low Small-Large isolated
## 2382          3  12      1013             3|13         low Small-Large isolated
## 2383          3  12      1013             3|13         low Small-Large isolated
## 2384          3  12      1013             3|13         low Small-Large isolated
## 2385          3  12      1013             3|13         low Small-Large isolated
## 2386          3  12      1013             3|13         low Small-Large isolated
## 2387          3  12      1013             3|13         low Small-Large isolated
## 2388          3  12      1013             3|13         low Small-Large isolated
## 2389          3  12      1013             3|13         low Small-Large isolated
## 2390          3  12      1013             3|13         low Small-Large isolated
## 2391          3  12      1013             3|13         low Small-Large isolated
## 2392          3  12      1013             3|13         low Small-Large isolated
## 2393          3  12      1013             3|13         low Small-Large isolated
## 2394          3  12      1013             3|13         low Small-Large isolated
## 2395          3  12      1013             3|13         low Small-Large isolated
## 2396          3  12      1013             3|13         low Small-Large isolated
## 2397          3  12      1013             3|13         low Small-Large isolated
## 2398          3  12      1013             3|13         low Small-Large isolated
## 2399          3  12      1013             3|13         low Small-Large isolated
## 2400          3  12      1013             3|13         low Small-Large isolated
## 2401          4  16      1013             3|13         low Small-Large isolated
## 2402          4  16      1013             3|13         low Small-Large isolated
## 2403          4  16      1013             3|13         low Small-Large isolated
## 2404          4  16      1013             3|13         low Small-Large isolated
## 2405          4  16      1013             3|13         low Small-Large isolated
## 2406          4  16      1013             3|13         low Small-Large isolated
## 2407          4  16      1013             3|13         low Small-Large isolated
## 2408          4  16      1013             3|13         low Small-Large isolated
## 2409          4  16      1013             3|13         low Small-Large isolated
## 2410          4  16      1013             3|13         low Small-Large isolated
## 2411          4  16      1013             3|13         low Small-Large isolated
## 2412          4  16      1013             3|13         low Small-Large isolated
## 2413          4  16      1013             3|13         low Small-Large isolated
## 2414          4  16      1013             3|13         low Small-Large isolated
## 2415          4  16      1013             3|13         low Small-Large isolated
## 2416          4  16      1013             3|13         low Small-Large isolated
## 2417          4  16      1013             3|13         low Small-Large isolated
## 2418          4  16      1013             3|13         low Small-Large isolated
## 2419          4  16      1013             3|13         low Small-Large isolated
## 2420          4  16      1013             3|13         low Small-Large isolated
## 2421          4  16      1013             3|13         low Small-Large isolated
## 2422          4  16      1013             3|13         low Small-Large isolated
## 2423          4  16      1013             3|13         low Small-Large isolated
## 2424          4  16      1013             3|13         low Small-Large isolated
## 2425          5  20      1013             3|13         low Small-Large isolated
## 2426          5  20      1013             3|13         low Small-Large isolated
## 2427          5  20      1013             3|13         low Small-Large isolated
## 2428          5  20      1013             3|13         low Small-Large isolated
## 2429          5  20      1013             3|13         low Small-Large isolated
## 2430          5  20      1013             3|13         low Small-Large isolated
## 2431          5  20      1013             3|13         low Small-Large isolated
## 2432          5  20      1013             3|13         low Small-Large isolated
## 2433          5  20      1013             3|13         low Small-Large isolated
## 2434          5  20      1013             3|13         low Small-Large isolated
## 2435          5  20      1013             3|13         low Small-Large isolated
## 2436          5  20      1013             3|13         low Small-Large isolated
## 2437          5  20      1013             3|13         low Small-Large isolated
## 2438          5  20      1013             3|13         low Small-Large isolated
## 2439          5  20      1013             3|13         low Small-Large isolated
## 2440          5  20      1013             3|13         low Small-Large isolated
## 2441          5  20      1013             3|13         low Small-Large isolated
## 2442          5  20      1013             3|13         low Small-Large isolated
## 2443          5  20      1013             3|13         low Small-Large isolated
## 2444          5  20      1013             3|13         low Small-Large isolated
## 2445          5  20      1013             3|13         low Small-Large isolated
## 2446          5  20      1013             3|13         low Small-Large isolated
## 2447          5  20      1013             3|13         low Small-Large isolated
## 2448          5  20      1013             3|13         low Small-Large isolated
## 2449          6  24      1013             3|13         low Small-Large isolated
## 2450          6  24      1013             3|13         low Small-Large isolated
## 2451          6  24      1013             3|13         low Small-Large isolated
## 2452          6  24      1013             3|13         low Small-Large isolated
## 2453          6  24      1013             3|13         low Small-Large isolated
## 2454          6  24      1013             3|13         low Small-Large isolated
## 2455          6  24      1013             3|13         low Small-Large isolated
## 2456          6  24      1013             3|13         low Small-Large isolated
## 2457          6  24      1013             3|13         low Small-Large isolated
## 2458          6  24      1013             3|13         low Small-Large isolated
## 2459          6  24      1013             3|13         low Small-Large isolated
## 2460          6  24      1013             3|13         low Small-Large isolated
## 2461          6  24      1013             3|13         low Small-Large isolated
## 2462          6  24      1013             3|13         low Small-Large isolated
## 2463          6  24      1013             3|13         low Small-Large isolated
## 2464          6  24      1013             3|13         low Small-Large isolated
## 2465          6  24      1013             3|13         low Small-Large isolated
## 2466          6  24      1013             3|13         low Small-Large isolated
## 2467          6  24      1013             3|13         low Small-Large isolated
## 2468          6  24      1013             3|13         low Small-Large isolated
## 2469          6  24      1013             3|13         low Small-Large isolated
## 2470          6  24      1013             3|13         low Small-Large isolated
## 2471          6  24      1013             3|13         low Small-Large isolated
## 2472          6  24      1013             3|13         low Small-Large isolated
## 2473          7  28      1013             3|13         low Small-Large isolated
## 2474          7  28      1013             3|13         low Small-Large isolated
## 2475          7  28      1013             3|13         low Small-Large isolated
## 2476          7  28      1013             3|13         low Small-Large isolated
## 2477          7  28      1013             3|13         low Small-Large isolated
## 2478          7  28      1013             3|13         low Small-Large isolated
## 2479          7  28      1013             3|13         low Small-Large isolated
## 2480          7  28      1013             3|13         low Small-Large isolated
## 2481          7  28      1013             3|13         low Small-Large isolated
## 2482          7  28      1013             3|13         low Small-Large isolated
## 2483          7  28      1013             3|13         low Small-Large isolated
## 2484          7  28      1013             3|13         low Small-Large isolated
## 2485          7  28      1013             3|13         low Small-Large isolated
## 2486          7  28      1013             3|13         low Small-Large isolated
## 2487          7  28      1013             3|13         low Small-Large isolated
## 2488          7  28      1013             3|13         low Small-Large isolated
## 2489          7  28      1013             3|13         low Small-Large isolated
## 2490          7  28      1013             3|13         low Small-Large isolated
## 2491          7  28      1013             3|13         low Small-Large isolated
## 2492          7  28      1013             3|13         low Small-Large isolated
## 2493          7  28      1013             3|13         low Small-Large isolated
## 2494          7  28      1013             3|13         low Small-Large isolated
## 2495          7  28      1013             3|13         low Small-Large isolated
## 2496          7  28      1013             3|13         low Small-Large isolated
## 2497          0   0      1014             3|14         low Small-Large isolated
## 2498          0   0      1014             3|14         low Small-Large isolated
## 2499          0   0      1014             3|14         low Small-Large isolated
## 2500          0   0      1014             3|14         low Small-Large isolated
## 2501          0   0      1014             3|14         low Small-Large isolated
## 2502          0   0      1014             3|14         low Small-Large isolated
## 2503          0   0      1014             3|14         low Small-Large isolated
## 2504          0   0      1014             3|14         low Small-Large isolated
## 2505          0   0      1014             3|14         low Small-Large isolated
## 2506          0   0      1014             3|14         low Small-Large isolated
## 2507          0   0      1014             3|14         low Small-Large isolated
## 2508          0   0      1014             3|14         low Small-Large isolated
## 2509          0   0      1014             3|14         low Small-Large isolated
## 2510          0   0      1014             3|14         low Small-Large isolated
## 2511          0   0      1014             3|14         low Small-Large isolated
## 2512          0   0      1014             3|14         low Small-Large isolated
## 2513          0   0      1014             3|14         low Small-Large isolated
## 2514          0   0      1014             3|14         low Small-Large isolated
## 2515          0   0      1014             3|14         low Small-Large isolated
## 2516          0   0      1014             3|14         low Small-Large isolated
## 2517          0   0      1014             3|14         low Small-Large isolated
## 2518          0   0      1014             3|14         low Small-Large isolated
## 2519          0   0      1014             3|14         low Small-Large isolated
## 2520          0   0      1014             3|14         low Small-Large isolated
## 2521          1   4      1014             3|14         low Small-Large isolated
## 2522          1   4      1014             3|14         low Small-Large isolated
## 2523          1   4      1014             3|14         low Small-Large isolated
## 2524          1   4      1014             3|14         low Small-Large isolated
## 2525          1   4      1014             3|14         low Small-Large isolated
## 2526          1   4      1014             3|14         low Small-Large isolated
## 2527          1   4      1014             3|14         low Small-Large isolated
## 2528          1   4      1014             3|14         low Small-Large isolated
## 2529          1   4      1014             3|14         low Small-Large isolated
## 2530          1   4      1014             3|14         low Small-Large isolated
## 2531          1   4      1014             3|14         low Small-Large isolated
## 2532          1   4      1014             3|14         low Small-Large isolated
## 2533          1   4      1014             3|14         low Small-Large isolated
## 2534          1   4      1014             3|14         low Small-Large isolated
## 2535          1   4      1014             3|14         low Small-Large isolated
## 2536          1   4      1014             3|14         low Small-Large isolated
## 2537          1   4      1014             3|14         low Small-Large isolated
## 2538          1   4      1014             3|14         low Small-Large isolated
## 2539          1   4      1014             3|14         low Small-Large isolated
## 2540          1   4      1014             3|14         low Small-Large isolated
## 2541          1   4      1014             3|14         low Small-Large isolated
## 2542          1   4      1014             3|14         low Small-Large isolated
## 2543          1   4      1014             3|14         low Small-Large isolated
## 2544          1   4      1014             3|14         low Small-Large isolated
## 2545          2   8      1014             3|14         low Small-Large isolated
## 2546          2   8      1014             3|14         low Small-Large isolated
## 2547          2   8      1014             3|14         low Small-Large isolated
## 2548          2   8      1014             3|14         low Small-Large isolated
## 2549          2   8      1014             3|14         low Small-Large isolated
## 2550          2   8      1014             3|14         low Small-Large isolated
## 2551          2   8      1014             3|14         low Small-Large isolated
## 2552          2   8      1014             3|14         low Small-Large isolated
## 2553          2   8      1014             3|14         low Small-Large isolated
## 2554          2   8      1014             3|14         low Small-Large isolated
## 2555          2   8      1014             3|14         low Small-Large isolated
## 2556          2   8      1014             3|14         low Small-Large isolated
## 2557          2   8      1014             3|14         low Small-Large isolated
## 2558          2   8      1014             3|14         low Small-Large isolated
## 2559          2   8      1014             3|14         low Small-Large isolated
## 2560          2   8      1014             3|14         low Small-Large isolated
## 2561          2   8      1014             3|14         low Small-Large isolated
## 2562          2   8      1014             3|14         low Small-Large isolated
## 2563          2   8      1014             3|14         low Small-Large isolated
## 2564          2   8      1014             3|14         low Small-Large isolated
## 2565          2   8      1014             3|14         low Small-Large isolated
## 2566          2   8      1014             3|14         low Small-Large isolated
## 2567          2   8      1014             3|14         low Small-Large isolated
## 2568          2   8      1014             3|14         low Small-Large isolated
## 2569          3  12      1014             3|14         low Small-Large isolated
## 2570          3  12      1014             3|14         low Small-Large isolated
## 2571          3  12      1014             3|14         low Small-Large isolated
## 2572          3  12      1014             3|14         low Small-Large isolated
## 2573          3  12      1014             3|14         low Small-Large isolated
## 2574          3  12      1014             3|14         low Small-Large isolated
## 2575          3  12      1014             3|14         low Small-Large isolated
## 2576          3  12      1014             3|14         low Small-Large isolated
## 2577          3  12      1014             3|14         low Small-Large isolated
## 2578          3  12      1014             3|14         low Small-Large isolated
## 2579          3  12      1014             3|14         low Small-Large isolated
## 2580          3  12      1014             3|14         low Small-Large isolated
## 2581          3  12      1014             3|14         low Small-Large isolated
## 2582          3  12      1014             3|14         low Small-Large isolated
## 2583          3  12      1014             3|14         low Small-Large isolated
## 2584          3  12      1014             3|14         low Small-Large isolated
## 2585          3  12      1014             3|14         low Small-Large isolated
## 2586          3  12      1014             3|14         low Small-Large isolated
## 2587          3  12      1014             3|14         low Small-Large isolated
## 2588          3  12      1014             3|14         low Small-Large isolated
## 2589          3  12      1014             3|14         low Small-Large isolated
## 2590          3  12      1014             3|14         low Small-Large isolated
## 2591          3  12      1014             3|14         low Small-Large isolated
## 2592          3  12      1014             3|14         low Small-Large isolated
## 2593          4  16      1014             3|14         low Small-Large isolated
## 2594          4  16      1014             3|14         low Small-Large isolated
## 2595          4  16      1014             3|14         low Small-Large isolated
## 2596          4  16      1014             3|14         low Small-Large isolated
## 2597          4  16      1014             3|14         low Small-Large isolated
## 2598          4  16      1014             3|14         low Small-Large isolated
## 2599          4  16      1014             3|14         low Small-Large isolated
## 2600          4  16      1014             3|14         low Small-Large isolated
## 2601          4  16      1014             3|14         low Small-Large isolated
## 2602          4  16      1014             3|14         low Small-Large isolated
## 2603          4  16      1014             3|14         low Small-Large isolated
## 2604          4  16      1014             3|14         low Small-Large isolated
## 2605          4  16      1014             3|14         low Small-Large isolated
## 2606          4  16      1014             3|14         low Small-Large isolated
## 2607          4  16      1014             3|14         low Small-Large isolated
## 2608          4  16      1014             3|14         low Small-Large isolated
## 2609          4  16      1014             3|14         low Small-Large isolated
## 2610          4  16      1014             3|14         low Small-Large isolated
## 2611          4  16      1014             3|14         low Small-Large isolated
## 2612          4  16      1014             3|14         low Small-Large isolated
## 2613          4  16      1014             3|14         low Small-Large isolated
## 2614          4  16      1014             3|14         low Small-Large isolated
## 2615          4  16      1014             3|14         low Small-Large isolated
## 2616          4  16      1014             3|14         low Small-Large isolated
## 2617          5  20      1014             3|14         low Small-Large isolated
## 2618          5  20      1014             3|14         low Small-Large isolated
## 2619          5  20      1014             3|14         low Small-Large isolated
## 2620          5  20      1014             3|14         low Small-Large isolated
## 2621          5  20      1014             3|14         low Small-Large isolated
## 2622          5  20      1014             3|14         low Small-Large isolated
## 2623          5  20      1014             3|14         low Small-Large isolated
## 2624          5  20      1014             3|14         low Small-Large isolated
## 2625          5  20      1014             3|14         low Small-Large isolated
## 2626          5  20      1014             3|14         low Small-Large isolated
## 2627          5  20      1014             3|14         low Small-Large isolated
## 2628          5  20      1014             3|14         low Small-Large isolated
## 2629          5  20      1014             3|14         low Small-Large isolated
## 2630          5  20      1014             3|14         low Small-Large isolated
## 2631          5  20      1014             3|14         low Small-Large isolated
## 2632          5  20      1014             3|14         low Small-Large isolated
## 2633          5  20      1014             3|14         low Small-Large isolated
## 2634          5  20      1014             3|14         low Small-Large isolated
## 2635          5  20      1014             3|14         low Small-Large isolated
## 2636          5  20      1014             3|14         low Small-Large isolated
## 2637          5  20      1014             3|14         low Small-Large isolated
## 2638          5  20      1014             3|14         low Small-Large isolated
## 2639          5  20      1014             3|14         low Small-Large isolated
## 2640          5  20      1014             3|14         low Small-Large isolated
## 2641          6  24      1014             3|14         low Small-Large isolated
## 2642          6  24      1014             3|14         low Small-Large isolated
## 2643          6  24      1014             3|14         low Small-Large isolated
## 2644          6  24      1014             3|14         low Small-Large isolated
## 2645          6  24      1014             3|14         low Small-Large isolated
## 2646          6  24      1014             3|14         low Small-Large isolated
## 2647          6  24      1014             3|14         low Small-Large isolated
## 2648          6  24      1014             3|14         low Small-Large isolated
## 2649          6  24      1014             3|14         low Small-Large isolated
## 2650          6  24      1014             3|14         low Small-Large isolated
## 2651          6  24      1014             3|14         low Small-Large isolated
## 2652          6  24      1014             3|14         low Small-Large isolated
## 2653          6  24      1014             3|14         low Small-Large isolated
## 2654          6  24      1014             3|14         low Small-Large isolated
## 2655          6  24      1014             3|14         low Small-Large isolated
## 2656          6  24      1014             3|14         low Small-Large isolated
## 2657          6  24      1014             3|14         low Small-Large isolated
## 2658          6  24      1014             3|14         low Small-Large isolated
## 2659          6  24      1014             3|14         low Small-Large isolated
## 2660          6  24      1014             3|14         low Small-Large isolated
## 2661          6  24      1014             3|14         low Small-Large isolated
## 2662          6  24      1014             3|14         low Small-Large isolated
## 2663          6  24      1014             3|14         low Small-Large isolated
## 2664          6  24      1014             3|14         low Small-Large isolated
## 2665          7  28      1014             3|14         low Small-Large isolated
## 2666          7  28      1014             3|14         low Small-Large isolated
## 2667          7  28      1014             3|14         low Small-Large isolated
## 2668          7  28      1014             3|14         low Small-Large isolated
## 2669          7  28      1014             3|14         low Small-Large isolated
## 2670          7  28      1014             3|14         low Small-Large isolated
## 2671          7  28      1014             3|14         low Small-Large isolated
## 2672          7  28      1014             3|14         low Small-Large isolated
## 2673          7  28      1014             3|14         low Small-Large isolated
## 2674          7  28      1014             3|14         low Small-Large isolated
## 2675          7  28      1014             3|14         low Small-Large isolated
## 2676          7  28      1014             3|14         low Small-Large isolated
## 2677          7  28      1014             3|14         low Small-Large isolated
## 2678          7  28      1014             3|14         low Small-Large isolated
## 2679          7  28      1014             3|14         low Small-Large isolated
## 2680          7  28      1014             3|14         low Small-Large isolated
## 2681          7  28      1014             3|14         low Small-Large isolated
## 2682          7  28      1014             3|14         low Small-Large isolated
## 2683          7  28      1014             3|14         low Small-Large isolated
## 2684          7  28      1014             3|14         low Small-Large isolated
## 2685          7  28      1014             3|14         low Small-Large isolated
## 2686          7  28      1014             3|14         low Small-Large isolated
## 2687          7  28      1014             3|14         low Small-Large isolated
## 2688          7  28      1014             3|14         low Small-Large isolated
## 2689          0   0      1015             3|15         low Small-Large isolated
## 2690          0   0      1015             3|15         low Small-Large isolated
## 2691          0   0      1015             3|15         low Small-Large isolated
## 2692          0   0      1015             3|15         low Small-Large isolated
## 2693          0   0      1015             3|15         low Small-Large isolated
## 2694          0   0      1015             3|15         low Small-Large isolated
## 2695          0   0      1015             3|15         low Small-Large isolated
## 2696          0   0      1015             3|15         low Small-Large isolated
## 2697          0   0      1015             3|15         low Small-Large isolated
## 2698          0   0      1015             3|15         low Small-Large isolated
## 2699          0   0      1015             3|15         low Small-Large isolated
## 2700          0   0      1015             3|15         low Small-Large isolated
## 2701          0   0      1015             3|15         low Small-Large isolated
## 2702          0   0      1015             3|15         low Small-Large isolated
## 2703          0   0      1015             3|15         low Small-Large isolated
## 2704          0   0      1015             3|15         low Small-Large isolated
## 2705          0   0      1015             3|15         low Small-Large isolated
## 2706          0   0      1015             3|15         low Small-Large isolated
## 2707          0   0      1015             3|15         low Small-Large isolated
## 2708          0   0      1015             3|15         low Small-Large isolated
## 2709          0   0      1015             3|15         low Small-Large isolated
## 2710          0   0      1015             3|15         low Small-Large isolated
## 2711          0   0      1015             3|15         low Small-Large isolated
## 2712          0   0      1015             3|15         low Small-Large isolated
## 2713          1   4      1015             3|15         low Small-Large isolated
## 2714          1   4      1015             3|15         low Small-Large isolated
## 2715          1   4      1015             3|15         low Small-Large isolated
## 2716          1   4      1015             3|15         low Small-Large isolated
## 2717          1   4      1015             3|15         low Small-Large isolated
## 2718          1   4      1015             3|15         low Small-Large isolated
## 2719          1   4      1015             3|15         low Small-Large isolated
## 2720          1   4      1015             3|15         low Small-Large isolated
## 2721          1   4      1015             3|15         low Small-Large isolated
## 2722          1   4      1015             3|15         low Small-Large isolated
## 2723          1   4      1015             3|15         low Small-Large isolated
## 2724          1   4      1015             3|15         low Small-Large isolated
## 2725          1   4      1015             3|15         low Small-Large isolated
## 2726          1   4      1015             3|15         low Small-Large isolated
## 2727          1   4      1015             3|15         low Small-Large isolated
## 2728          1   4      1015             3|15         low Small-Large isolated
## 2729          1   4      1015             3|15         low Small-Large isolated
## 2730          1   4      1015             3|15         low Small-Large isolated
## 2731          1   4      1015             3|15         low Small-Large isolated
## 2732          1   4      1015             3|15         low Small-Large isolated
## 2733          1   4      1015             3|15         low Small-Large isolated
## 2734          1   4      1015             3|15         low Small-Large isolated
## 2735          1   4      1015             3|15         low Small-Large isolated
## 2736          1   4      1015             3|15         low Small-Large isolated
## 2737          2   8      1015             3|15         low Small-Large isolated
## 2738          2   8      1015             3|15         low Small-Large isolated
## 2739          2   8      1015             3|15         low Small-Large isolated
## 2740          2   8      1015             3|15         low Small-Large isolated
## 2741          2   8      1015             3|15         low Small-Large isolated
## 2742          2   8      1015             3|15         low Small-Large isolated
## 2743          2   8      1015             3|15         low Small-Large isolated
## 2744          2   8      1015             3|15         low Small-Large isolated
## 2745          2   8      1015             3|15         low Small-Large isolated
## 2746          2   8      1015             3|15         low Small-Large isolated
## 2747          2   8      1015             3|15         low Small-Large isolated
## 2748          2   8      1015             3|15         low Small-Large isolated
## 2749          2   8      1015             3|15         low Small-Large isolated
## 2750          2   8      1015             3|15         low Small-Large isolated
## 2751          2   8      1015             3|15         low Small-Large isolated
## 2752          2   8      1015             3|15         low Small-Large isolated
## 2753          2   8      1015             3|15         low Small-Large isolated
## 2754          2   8      1015             3|15         low Small-Large isolated
## 2755          2   8      1015             3|15         low Small-Large isolated
## 2756          2   8      1015             3|15         low Small-Large isolated
## 2757          2   8      1015             3|15         low Small-Large isolated
## 2758          2   8      1015             3|15         low Small-Large isolated
## 2759          2   8      1015             3|15         low Small-Large isolated
## 2760          2   8      1015             3|15         low Small-Large isolated
## 2761          3  12      1015             3|15         low Small-Large isolated
## 2762          3  12      1015             3|15         low Small-Large isolated
## 2763          3  12      1015             3|15         low Small-Large isolated
## 2764          3  12      1015             3|15         low Small-Large isolated
## 2765          3  12      1015             3|15         low Small-Large isolated
## 2766          3  12      1015             3|15         low Small-Large isolated
## 2767          3  12      1015             3|15         low Small-Large isolated
## 2768          3  12      1015             3|15         low Small-Large isolated
## 2769          3  12      1015             3|15         low Small-Large isolated
## 2770          3  12      1015             3|15         low Small-Large isolated
## 2771          3  12      1015             3|15         low Small-Large isolated
## 2772          3  12      1015             3|15         low Small-Large isolated
## 2773          3  12      1015             3|15         low Small-Large isolated
## 2774          3  12      1015             3|15         low Small-Large isolated
## 2775          3  12      1015             3|15         low Small-Large isolated
## 2776          3  12      1015             3|15         low Small-Large isolated
## 2777          3  12      1015             3|15         low Small-Large isolated
## 2778          3  12      1015             3|15         low Small-Large isolated
## 2779          3  12      1015             3|15         low Small-Large isolated
## 2780          3  12      1015             3|15         low Small-Large isolated
## 2781          3  12      1015             3|15         low Small-Large isolated
## 2782          3  12      1015             3|15         low Small-Large isolated
## 2783          3  12      1015             3|15         low Small-Large isolated
## 2784          3  12      1015             3|15         low Small-Large isolated
## 2785          4  16      1015             3|15         low Small-Large isolated
## 2786          4  16      1015             3|15         low Small-Large isolated
## 2787          4  16      1015             3|15         low Small-Large isolated
## 2788          4  16      1015             3|15         low Small-Large isolated
## 2789          4  16      1015             3|15         low Small-Large isolated
## 2790          4  16      1015             3|15         low Small-Large isolated
## 2791          4  16      1015             3|15         low Small-Large isolated
## 2792          4  16      1015             3|15         low Small-Large isolated
## 2793          4  16      1015             3|15         low Small-Large isolated
## 2794          4  16      1015             3|15         low Small-Large isolated
## 2795          4  16      1015             3|15         low Small-Large isolated
## 2796          4  16      1015             3|15         low Small-Large isolated
## 2797          4  16      1015             3|15         low Small-Large isolated
## 2798          4  16      1015             3|15         low Small-Large isolated
## 2799          4  16      1015             3|15         low Small-Large isolated
## 2800          4  16      1015             3|15         low Small-Large isolated
## 2801          4  16      1015             3|15         low Small-Large isolated
## 2802          4  16      1015             3|15         low Small-Large isolated
## 2803          4  16      1015             3|15         low Small-Large isolated
## 2804          4  16      1015             3|15         low Small-Large isolated
## 2805          4  16      1015             3|15         low Small-Large isolated
## 2806          4  16      1015             3|15         low Small-Large isolated
## 2807          4  16      1015             3|15         low Small-Large isolated
## 2808          4  16      1015             3|15         low Small-Large isolated
## 2809          5  20      1015             3|15         low Small-Large isolated
## 2810          5  20      1015             3|15         low Small-Large isolated
## 2811          5  20      1015             3|15         low Small-Large isolated
## 2812          5  20      1015             3|15         low Small-Large isolated
## 2813          5  20      1015             3|15         low Small-Large isolated
## 2814          5  20      1015             3|15         low Small-Large isolated
## 2815          5  20      1015             3|15         low Small-Large isolated
## 2816          5  20      1015             3|15         low Small-Large isolated
## 2817          5  20      1015             3|15         low Small-Large isolated
## 2818          5  20      1015             3|15         low Small-Large isolated
## 2819          5  20      1015             3|15         low Small-Large isolated
## 2820          5  20      1015             3|15         low Small-Large isolated
## 2821          5  20      1015             3|15         low Small-Large isolated
## 2822          5  20      1015             3|15         low Small-Large isolated
## 2823          5  20      1015             3|15         low Small-Large isolated
## 2824          5  20      1015             3|15         low Small-Large isolated
## 2825          5  20      1015             3|15         low Small-Large isolated
## 2826          5  20      1015             3|15         low Small-Large isolated
## 2827          5  20      1015             3|15         low Small-Large isolated
## 2828          5  20      1015             3|15         low Small-Large isolated
## 2829          5  20      1015             3|15         low Small-Large isolated
## 2830          5  20      1015             3|15         low Small-Large isolated
## 2831          5  20      1015             3|15         low Small-Large isolated
## 2832          5  20      1015             3|15         low Small-Large isolated
## 2833          6  24      1015             3|15         low Small-Large isolated
## 2834          6  24      1015             3|15         low Small-Large isolated
## 2835          6  24      1015             3|15         low Small-Large isolated
## 2836          6  24      1015             3|15         low Small-Large isolated
## 2837          6  24      1015             3|15         low Small-Large isolated
## 2838          6  24      1015             3|15         low Small-Large isolated
## 2839          6  24      1015             3|15         low Small-Large isolated
## 2840          6  24      1015             3|15         low Small-Large isolated
## 2841          6  24      1015             3|15         low Small-Large isolated
## 2842          6  24      1015             3|15         low Small-Large isolated
## 2843          6  24      1015             3|15         low Small-Large isolated
## 2844          6  24      1015             3|15         low Small-Large isolated
## 2845          6  24      1015             3|15         low Small-Large isolated
## 2846          6  24      1015             3|15         low Small-Large isolated
## 2847          6  24      1015             3|15         low Small-Large isolated
## 2848          6  24      1015             3|15         low Small-Large isolated
## 2849          6  24      1015             3|15         low Small-Large isolated
## 2850          6  24      1015             3|15         low Small-Large isolated
## 2851          6  24      1015             3|15         low Small-Large isolated
## 2852          6  24      1015             3|15         low Small-Large isolated
## 2853          6  24      1015             3|15         low Small-Large isolated
## 2854          6  24      1015             3|15         low Small-Large isolated
## 2855          6  24      1015             3|15         low Small-Large isolated
## 2856          6  24      1015             3|15         low Small-Large isolated
## 2857          7  28      1015             3|15         low Small-Large isolated
## 2858          7  28      1015             3|15         low Small-Large isolated
## 2859          7  28      1015             3|15         low Small-Large isolated
## 2860          7  28      1015             3|15         low Small-Large isolated
## 2861          7  28      1015             3|15         low Small-Large isolated
## 2862          7  28      1015             3|15         low Small-Large isolated
## 2863          7  28      1015             3|15         low Small-Large isolated
## 2864          7  28      1015             3|15         low Small-Large isolated
## 2865          7  28      1015             3|15         low Small-Large isolated
## 2866          7  28      1015             3|15         low Small-Large isolated
## 2867          7  28      1015             3|15         low Small-Large isolated
## 2868          7  28      1015             3|15         low Small-Large isolated
## 2869          7  28      1015             3|15         low Small-Large isolated
## 2870          7  28      1015             3|15         low Small-Large isolated
## 2871          7  28      1015             3|15         low Small-Large isolated
## 2872          7  28      1015             3|15         low Small-Large isolated
## 2873          7  28      1015             3|15         low Small-Large isolated
## 2874          7  28      1015             3|15         low Small-Large isolated
## 2875          7  28      1015             3|15         low Small-Large isolated
## 2876          7  28      1015             3|15         low Small-Large isolated
## 2877          7  28      1015             3|15         low Small-Large isolated
## 2878          7  28      1015             3|15         low Small-Large isolated
## 2879          7  28      1015             3|15         low Small-Large isolated
## 2880          7  28      1015             3|15         low Small-Large isolated
## 2881          0   0      1016             4|11         low Small-Large isolated
## 2882          0   0      1016             4|11         low Small-Large isolated
## 2883          0   0      1016             4|11         low Small-Large isolated
## 2884          0   0      1016             4|11         low Small-Large isolated
## 2885          0   0      1016             4|11         low Small-Large isolated
## 2886          0   0      1016             4|11         low Small-Large isolated
## 2887          0   0      1016             4|11         low Small-Large isolated
## 2888          0   0      1016             4|11         low Small-Large isolated
## 2889          0   0      1016             4|11         low Small-Large isolated
## 2890          0   0      1016             4|11         low Small-Large isolated
## 2891          0   0      1016             4|11         low Small-Large isolated
## 2892          0   0      1016             4|11         low Small-Large isolated
## 2893          0   0      1016             4|11         low Small-Large isolated
## 2894          0   0      1016             4|11         low Small-Large isolated
## 2895          0   0      1016             4|11         low Small-Large isolated
## 2896          0   0      1016             4|11         low Small-Large isolated
## 2897          0   0      1016             4|11         low Small-Large isolated
## 2898          0   0      1016             4|11         low Small-Large isolated
## 2899          0   0      1016             4|11         low Small-Large isolated
## 2900          0   0      1016             4|11         low Small-Large isolated
## 2901          0   0      1016             4|11         low Small-Large isolated
## 2902          0   0      1016             4|11         low Small-Large isolated
## 2903          0   0      1016             4|11         low Small-Large isolated
## 2904          0   0      1016             4|11         low Small-Large isolated
## 2905          1   4      1016             4|11         low Small-Large isolated
## 2906          1   4      1016             4|11         low Small-Large isolated
## 2907          1   4      1016             4|11         low Small-Large isolated
## 2908          1   4      1016             4|11         low Small-Large isolated
## 2909          1   4      1016             4|11         low Small-Large isolated
## 2910          1   4      1016             4|11         low Small-Large isolated
## 2911          1   4      1016             4|11         low Small-Large isolated
## 2912          1   4      1016             4|11         low Small-Large isolated
## 2913          1   4      1016             4|11         low Small-Large isolated
## 2914          1   4      1016             4|11         low Small-Large isolated
## 2915          1   4      1016             4|11         low Small-Large isolated
## 2916          1   4      1016             4|11         low Small-Large isolated
## 2917          1   4      1016             4|11         low Small-Large isolated
## 2918          1   4      1016             4|11         low Small-Large isolated
## 2919          1   4      1016             4|11         low Small-Large isolated
## 2920          1   4      1016             4|11         low Small-Large isolated
## 2921          1   4      1016             4|11         low Small-Large isolated
## 2922          1   4      1016             4|11         low Small-Large isolated
## 2923          1   4      1016             4|11         low Small-Large isolated
## 2924          1   4      1016             4|11         low Small-Large isolated
## 2925          1   4      1016             4|11         low Small-Large isolated
## 2926          1   4      1016             4|11         low Small-Large isolated
## 2927          1   4      1016             4|11         low Small-Large isolated
## 2928          1   4      1016             4|11         low Small-Large isolated
## 2929          2   8      1016             4|11         low Small-Large isolated
## 2930          2   8      1016             4|11         low Small-Large isolated
## 2931          2   8      1016             4|11         low Small-Large isolated
## 2932          2   8      1016             4|11         low Small-Large isolated
## 2933          2   8      1016             4|11         low Small-Large isolated
## 2934          2   8      1016             4|11         low Small-Large isolated
## 2935          2   8      1016             4|11         low Small-Large isolated
## 2936          2   8      1016             4|11         low Small-Large isolated
## 2937          2   8      1016             4|11         low Small-Large isolated
## 2938          2   8      1016             4|11         low Small-Large isolated
## 2939          2   8      1016             4|11         low Small-Large isolated
## 2940          2   8      1016             4|11         low Small-Large isolated
## 2941          2   8      1016             4|11         low Small-Large isolated
## 2942          2   8      1016             4|11         low Small-Large isolated
## 2943          2   8      1016             4|11         low Small-Large isolated
## 2944          2   8      1016             4|11         low Small-Large isolated
## 2945          2   8      1016             4|11         low Small-Large isolated
## 2946          2   8      1016             4|11         low Small-Large isolated
## 2947          2   8      1016             4|11         low Small-Large isolated
## 2948          2   8      1016             4|11         low Small-Large isolated
## 2949          2   8      1016             4|11         low Small-Large isolated
## 2950          2   8      1016             4|11         low Small-Large isolated
## 2951          2   8      1016             4|11         low Small-Large isolated
## 2952          2   8      1016             4|11         low Small-Large isolated
## 2953          3  12      1016             4|11         low Small-Large isolated
## 2954          3  12      1016             4|11         low Small-Large isolated
## 2955          3  12      1016             4|11         low Small-Large isolated
## 2956          3  12      1016             4|11         low Small-Large isolated
## 2957          3  12      1016             4|11         low Small-Large isolated
## 2958          3  12      1016             4|11         low Small-Large isolated
## 2959          3  12      1016             4|11         low Small-Large isolated
## 2960          3  12      1016             4|11         low Small-Large isolated
## 2961          3  12      1016             4|11         low Small-Large isolated
## 2962          3  12      1016             4|11         low Small-Large isolated
## 2963          3  12      1016             4|11         low Small-Large isolated
## 2964          3  12      1016             4|11         low Small-Large isolated
## 2965          3  12      1016             4|11         low Small-Large isolated
## 2966          3  12      1016             4|11         low Small-Large isolated
## 2967          3  12      1016             4|11         low Small-Large isolated
## 2968          3  12      1016             4|11         low Small-Large isolated
## 2969          3  12      1016             4|11         low Small-Large isolated
## 2970          3  12      1016             4|11         low Small-Large isolated
## 2971          3  12      1016             4|11         low Small-Large isolated
## 2972          3  12      1016             4|11         low Small-Large isolated
## 2973          3  12      1016             4|11         low Small-Large isolated
## 2974          3  12      1016             4|11         low Small-Large isolated
## 2975          3  12      1016             4|11         low Small-Large isolated
## 2976          3  12      1016             4|11         low Small-Large isolated
## 2977          4  16      1016             4|11         low Small-Large isolated
## 2978          4  16      1016             4|11         low Small-Large isolated
## 2979          4  16      1016             4|11         low Small-Large isolated
## 2980          4  16      1016             4|11         low Small-Large isolated
## 2981          4  16      1016             4|11         low Small-Large isolated
## 2982          4  16      1016             4|11         low Small-Large isolated
## 2983          4  16      1016             4|11         low Small-Large isolated
## 2984          4  16      1016             4|11         low Small-Large isolated
## 2985          4  16      1016             4|11         low Small-Large isolated
## 2986          4  16      1016             4|11         low Small-Large isolated
## 2987          4  16      1016             4|11         low Small-Large isolated
## 2988          4  16      1016             4|11         low Small-Large isolated
## 2989          4  16      1016             4|11         low Small-Large isolated
## 2990          4  16      1016             4|11         low Small-Large isolated
## 2991          4  16      1016             4|11         low Small-Large isolated
## 2992          4  16      1016             4|11         low Small-Large isolated
## 2993          4  16      1016             4|11         low Small-Large isolated
## 2994          4  16      1016             4|11         low Small-Large isolated
## 2995          4  16      1016             4|11         low Small-Large isolated
## 2996          4  16      1016             4|11         low Small-Large isolated
## 2997          4  16      1016             4|11         low Small-Large isolated
## 2998          4  16      1016             4|11         low Small-Large isolated
## 2999          4  16      1016             4|11         low Small-Large isolated
## 3000          4  16      1016             4|11         low Small-Large isolated
## 3001          5  20      1016             4|11         low Small-Large isolated
## 3002          5  20      1016             4|11         low Small-Large isolated
## 3003          5  20      1016             4|11         low Small-Large isolated
## 3004          5  20      1016             4|11         low Small-Large isolated
## 3005          5  20      1016             4|11         low Small-Large isolated
## 3006          5  20      1016             4|11         low Small-Large isolated
## 3007          5  20      1016             4|11         low Small-Large isolated
## 3008          5  20      1016             4|11         low Small-Large isolated
## 3009          5  20      1016             4|11         low Small-Large isolated
## 3010          5  20      1016             4|11         low Small-Large isolated
## 3011          5  20      1016             4|11         low Small-Large isolated
## 3012          5  20      1016             4|11         low Small-Large isolated
## 3013          5  20      1016             4|11         low Small-Large isolated
## 3014          5  20      1016             4|11         low Small-Large isolated
## 3015          5  20      1016             4|11         low Small-Large isolated
## 3016          5  20      1016             4|11         low Small-Large isolated
## 3017          5  20      1016             4|11         low Small-Large isolated
## 3018          5  20      1016             4|11         low Small-Large isolated
## 3019          5  20      1016             4|11         low Small-Large isolated
## 3020          5  20      1016             4|11         low Small-Large isolated
## 3021          5  20      1016             4|11         low Small-Large isolated
## 3022          5  20      1016             4|11         low Small-Large isolated
## 3023          5  20      1016             4|11         low Small-Large isolated
## 3024          5  20      1016             4|11         low Small-Large isolated
## 3025          6  24      1016             4|11         low Small-Large isolated
## 3026          6  24      1016             4|11         low Small-Large isolated
## 3027          6  24      1016             4|11         low Small-Large isolated
## 3028          6  24      1016             4|11         low Small-Large isolated
## 3029          6  24      1016             4|11         low Small-Large isolated
## 3030          6  24      1016             4|11         low Small-Large isolated
## 3031          6  24      1016             4|11         low Small-Large isolated
## 3032          6  24      1016             4|11         low Small-Large isolated
## 3033          6  24      1016             4|11         low Small-Large isolated
## 3034          6  24      1016             4|11         low Small-Large isolated
## 3035          6  24      1016             4|11         low Small-Large isolated
## 3036          6  24      1016             4|11         low Small-Large isolated
## 3037          6  24      1016             4|11         low Small-Large isolated
## 3038          6  24      1016             4|11         low Small-Large isolated
## 3039          6  24      1016             4|11         low Small-Large isolated
## 3040          6  24      1016             4|11         low Small-Large isolated
## 3041          6  24      1016             4|11         low Small-Large isolated
## 3042          6  24      1016             4|11         low Small-Large isolated
## 3043          6  24      1016             4|11         low Small-Large isolated
## 3044          6  24      1016             4|11         low Small-Large isolated
## 3045          6  24      1016             4|11         low Small-Large isolated
## 3046          6  24      1016             4|11         low Small-Large isolated
## 3047          6  24      1016             4|11         low Small-Large isolated
## 3048          6  24      1016             4|11         low Small-Large isolated
## 3049          7  28      1016             4|11         low Small-Large isolated
## 3050          7  28      1016             4|11         low Small-Large isolated
## 3051          7  28      1016             4|11         low Small-Large isolated
## 3052          7  28      1016             4|11         low Small-Large isolated
## 3053          7  28      1016             4|11         low Small-Large isolated
## 3054          7  28      1016             4|11         low Small-Large isolated
## 3055          7  28      1016             4|11         low Small-Large isolated
## 3056          7  28      1016             4|11         low Small-Large isolated
## 3057          7  28      1016             4|11         low Small-Large isolated
## 3058          7  28      1016             4|11         low Small-Large isolated
## 3059          7  28      1016             4|11         low Small-Large isolated
## 3060          7  28      1016             4|11         low Small-Large isolated
## 3061          7  28      1016             4|11         low Small-Large isolated
## 3062          7  28      1016             4|11         low Small-Large isolated
## 3063          7  28      1016             4|11         low Small-Large isolated
## 3064          7  28      1016             4|11         low Small-Large isolated
## 3065          7  28      1016             4|11         low Small-Large isolated
## 3066          7  28      1016             4|11         low Small-Large isolated
## 3067          7  28      1016             4|11         low Small-Large isolated
## 3068          7  28      1016             4|11         low Small-Large isolated
## 3069          7  28      1016             4|11         low Small-Large isolated
## 3070          7  28      1016             4|11         low Small-Large isolated
## 3071          7  28      1016             4|11         low Small-Large isolated
## 3072          7  28      1016             4|11         low Small-Large isolated
## 3073          0   0      1017             4|12         low Small-Large isolated
## 3074          0   0      1017             4|12         low Small-Large isolated
## 3075          0   0      1017             4|12         low Small-Large isolated
## 3076          0   0      1017             4|12         low Small-Large isolated
## 3077          0   0      1017             4|12         low Small-Large isolated
## 3078          0   0      1017             4|12         low Small-Large isolated
## 3079          0   0      1017             4|12         low Small-Large isolated
## 3080          0   0      1017             4|12         low Small-Large isolated
## 3081          0   0      1017             4|12         low Small-Large isolated
## 3082          0   0      1017             4|12         low Small-Large isolated
## 3083          0   0      1017             4|12         low Small-Large isolated
## 3084          0   0      1017             4|12         low Small-Large isolated
## 3085          0   0      1017             4|12         low Small-Large isolated
## 3086          0   0      1017             4|12         low Small-Large isolated
## 3087          0   0      1017             4|12         low Small-Large isolated
## 3088          0   0      1017             4|12         low Small-Large isolated
## 3089          0   0      1017             4|12         low Small-Large isolated
## 3090          0   0      1017             4|12         low Small-Large isolated
## 3091          0   0      1017             4|12         low Small-Large isolated
## 3092          0   0      1017             4|12         low Small-Large isolated
## 3093          0   0      1017             4|12         low Small-Large isolated
## 3094          0   0      1017             4|12         low Small-Large isolated
## 3095          0   0      1017             4|12         low Small-Large isolated
## 3096          0   0      1017             4|12         low Small-Large isolated
## 3097          1   4      1017             4|12         low Small-Large isolated
## 3098          1   4      1017             4|12         low Small-Large isolated
## 3099          1   4      1017             4|12         low Small-Large isolated
## 3100          1   4      1017             4|12         low Small-Large isolated
## 3101          1   4      1017             4|12         low Small-Large isolated
## 3102          1   4      1017             4|12         low Small-Large isolated
## 3103          1   4      1017             4|12         low Small-Large isolated
## 3104          1   4      1017             4|12         low Small-Large isolated
## 3105          1   4      1017             4|12         low Small-Large isolated
## 3106          1   4      1017             4|12         low Small-Large isolated
## 3107          1   4      1017             4|12         low Small-Large isolated
## 3108          1   4      1017             4|12         low Small-Large isolated
## 3109          1   4      1017             4|12         low Small-Large isolated
## 3110          1   4      1017             4|12         low Small-Large isolated
## 3111          1   4      1017             4|12         low Small-Large isolated
## 3112          1   4      1017             4|12         low Small-Large isolated
## 3113          1   4      1017             4|12         low Small-Large isolated
## 3114          1   4      1017             4|12         low Small-Large isolated
## 3115          1   4      1017             4|12         low Small-Large isolated
## 3116          1   4      1017             4|12         low Small-Large isolated
## 3117          1   4      1017             4|12         low Small-Large isolated
## 3118          1   4      1017             4|12         low Small-Large isolated
## 3119          1   4      1017             4|12         low Small-Large isolated
## 3120          1   4      1017             4|12         low Small-Large isolated
## 3121          2   8      1017             4|12         low Small-Large isolated
## 3122          2   8      1017             4|12         low Small-Large isolated
## 3123          2   8      1017             4|12         low Small-Large isolated
## 3124          2   8      1017             4|12         low Small-Large isolated
## 3125          2   8      1017             4|12         low Small-Large isolated
## 3126          2   8      1017             4|12         low Small-Large isolated
## 3127          2   8      1017             4|12         low Small-Large isolated
## 3128          2   8      1017             4|12         low Small-Large isolated
## 3129          2   8      1017             4|12         low Small-Large isolated
## 3130          2   8      1017             4|12         low Small-Large isolated
## 3131          2   8      1017             4|12         low Small-Large isolated
## 3132          2   8      1017             4|12         low Small-Large isolated
## 3133          2   8      1017             4|12         low Small-Large isolated
## 3134          2   8      1017             4|12         low Small-Large isolated
## 3135          2   8      1017             4|12         low Small-Large isolated
## 3136          2   8      1017             4|12         low Small-Large isolated
## 3137          2   8      1017             4|12         low Small-Large isolated
## 3138          2   8      1017             4|12         low Small-Large isolated
## 3139          2   8      1017             4|12         low Small-Large isolated
## 3140          2   8      1017             4|12         low Small-Large isolated
## 3141          2   8      1017             4|12         low Small-Large isolated
## 3142          2   8      1017             4|12         low Small-Large isolated
## 3143          2   8      1017             4|12         low Small-Large isolated
## 3144          2   8      1017             4|12         low Small-Large isolated
## 3145          3  12      1017             4|12         low Small-Large isolated
## 3146          3  12      1017             4|12         low Small-Large isolated
## 3147          3  12      1017             4|12         low Small-Large isolated
## 3148          3  12      1017             4|12         low Small-Large isolated
## 3149          3  12      1017             4|12         low Small-Large isolated
## 3150          3  12      1017             4|12         low Small-Large isolated
## 3151          3  12      1017             4|12         low Small-Large isolated
## 3152          3  12      1017             4|12         low Small-Large isolated
## 3153          3  12      1017             4|12         low Small-Large isolated
## 3154          3  12      1017             4|12         low Small-Large isolated
## 3155          3  12      1017             4|12         low Small-Large isolated
## 3156          3  12      1017             4|12         low Small-Large isolated
## 3157          3  12      1017             4|12         low Small-Large isolated
## 3158          3  12      1017             4|12         low Small-Large isolated
## 3159          3  12      1017             4|12         low Small-Large isolated
## 3160          3  12      1017             4|12         low Small-Large isolated
## 3161          3  12      1017             4|12         low Small-Large isolated
## 3162          3  12      1017             4|12         low Small-Large isolated
## 3163          3  12      1017             4|12         low Small-Large isolated
## 3164          3  12      1017             4|12         low Small-Large isolated
## 3165          3  12      1017             4|12         low Small-Large isolated
## 3166          3  12      1017             4|12         low Small-Large isolated
## 3167          3  12      1017             4|12         low Small-Large isolated
## 3168          3  12      1017             4|12         low Small-Large isolated
## 3169          4  16      1017             4|12         low Small-Large isolated
## 3170          4  16      1017             4|12         low Small-Large isolated
## 3171          4  16      1017             4|12         low Small-Large isolated
## 3172          4  16      1017             4|12         low Small-Large isolated
## 3173          4  16      1017             4|12         low Small-Large isolated
## 3174          4  16      1017             4|12         low Small-Large isolated
## 3175          4  16      1017             4|12         low Small-Large isolated
## 3176          4  16      1017             4|12         low Small-Large isolated
## 3177          4  16      1017             4|12         low Small-Large isolated
## 3178          4  16      1017             4|12         low Small-Large isolated
## 3179          4  16      1017             4|12         low Small-Large isolated
## 3180          4  16      1017             4|12         low Small-Large isolated
## 3181          4  16      1017             4|12         low Small-Large isolated
## 3182          4  16      1017             4|12         low Small-Large isolated
## 3183          4  16      1017             4|12         low Small-Large isolated
## 3184          4  16      1017             4|12         low Small-Large isolated
## 3185          4  16      1017             4|12         low Small-Large isolated
## 3186          4  16      1017             4|12         low Small-Large isolated
## 3187          4  16      1017             4|12         low Small-Large isolated
## 3188          4  16      1017             4|12         low Small-Large isolated
## 3189          4  16      1017             4|12         low Small-Large isolated
## 3190          4  16      1017             4|12         low Small-Large isolated
## 3191          4  16      1017             4|12         low Small-Large isolated
## 3192          4  16      1017             4|12         low Small-Large isolated
## 3193          5  20      1017             4|12         low Small-Large isolated
## 3194          5  20      1017             4|12         low Small-Large isolated
## 3195          5  20      1017             4|12         low Small-Large isolated
## 3196          5  20      1017             4|12         low Small-Large isolated
## 3197          5  20      1017             4|12         low Small-Large isolated
## 3198          5  20      1017             4|12         low Small-Large isolated
## 3199          5  20      1017             4|12         low Small-Large isolated
## 3200          5  20      1017             4|12         low Small-Large isolated
## 3201          5  20      1017             4|12         low Small-Large isolated
## 3202          5  20      1017             4|12         low Small-Large isolated
## 3203          5  20      1017             4|12         low Small-Large isolated
## 3204          5  20      1017             4|12         low Small-Large isolated
## 3205          5  20      1017             4|12         low Small-Large isolated
## 3206          5  20      1017             4|12         low Small-Large isolated
## 3207          5  20      1017             4|12         low Small-Large isolated
## 3208          5  20      1017             4|12         low Small-Large isolated
## 3209          5  20      1017             4|12         low Small-Large isolated
## 3210          5  20      1017             4|12         low Small-Large isolated
## 3211          5  20      1017             4|12         low Small-Large isolated
## 3212          5  20      1017             4|12         low Small-Large isolated
## 3213          5  20      1017             4|12         low Small-Large isolated
## 3214          5  20      1017             4|12         low Small-Large isolated
## 3215          5  20      1017             4|12         low Small-Large isolated
## 3216          5  20      1017             4|12         low Small-Large isolated
## 3217          6  24      1017             4|12         low Small-Large isolated
## 3218          6  24      1017             4|12         low Small-Large isolated
## 3219          6  24      1017             4|12         low Small-Large isolated
## 3220          6  24      1017             4|12         low Small-Large isolated
## 3221          6  24      1017             4|12         low Small-Large isolated
## 3222          6  24      1017             4|12         low Small-Large isolated
## 3223          6  24      1017             4|12         low Small-Large isolated
## 3224          6  24      1017             4|12         low Small-Large isolated
## 3225          6  24      1017             4|12         low Small-Large isolated
##      mean_shannon mean_richness jaccard_index bray_curtis beta_spatial_turnover
## 1       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 4       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 5       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 6       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 7       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 8       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 9       2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 10      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 11      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 12      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 13      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 14      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 15      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 16      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 17      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 18      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 19      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 20      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 21      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 22      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 23      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 24      2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 25      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 26      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 27      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 28      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 29      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 30      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 31      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 32      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 33      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 34      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 35      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 36      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 37      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 38      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 39      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 40      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 41      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 42      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 43      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 44      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 45      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 46      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 47      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 48      1.5003642           7.5     0.3797327   0.2343643             0.1428571
## 49      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 50      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 51      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 52      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 53      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 54      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 55      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 56      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 57      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 58      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 59      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 60      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 61      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 62      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 63      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 64      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 65      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 66      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 67      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 68      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 69      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 70      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 71      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 72      1.5643953           8.0     0.5027346   0.3357686             0.1250000
## 73      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 74      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 75      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 76      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 77      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 78      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 79      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 80      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 81      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 82      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 83      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 84      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 85      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 86      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 87      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 88      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 89      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 90      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 91      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 92      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 93      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 94      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 95      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 96      1.6095690           6.0     0.8561835   0.7485323             0.0000000
## 97      1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 98      1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 99      1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 100     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 101     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 102     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 103     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 104     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 105     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 106     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 107     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 108     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 109     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 110     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 111     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 112     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 113     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 114     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 115     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 116     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 117     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 118     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 119     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 120     1.8022460           7.5     0.4038401   0.2530073             0.1428571
## 121     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 122     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 123     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 124     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 125     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 126     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 127     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 128     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 129     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 130     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 131     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 132     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 133     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 134     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 135     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 136     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 137     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 138     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 139     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 140     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 141     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 142     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 143     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 144     0.9046531           4.0     0.8022244   0.6697618             0.0000000
## 145     1.1722772           6.0     0.8009542   0.6679930             0.4000000
## 146     1.1722772           6.0     0.8009542   0.6679930             0.4000000
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## 415     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 416     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 417     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 418     1.6354094           8.0     0.5434531   0.3731106             0.1428571
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## 420     1.6354094           8.0     0.5434531   0.3731106             0.1428571
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## 423     1.6354094           8.0     0.5434531   0.3731106             0.1428571
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## 425     1.6354094           8.0     0.5434531   0.3731106             0.1428571
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## 427     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 428     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 429     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 430     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 431     1.6354094           8.0     0.5434531   0.3731106             0.1428571
## 432     1.6354094           8.0     0.5434531   0.3731106             0.1428571
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## 435     1.7043816           8.5     0.4219277   0.2673691             0.1250000
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## 437     1.7043816           8.5     0.4219277   0.2673691             0.1250000
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## 439     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 440     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 441     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 442     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 443     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 444     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 445     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 446     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 447     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 448     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 449     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 450     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 451     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 452     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 453     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 454     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 455     1.7043816           8.5     0.4219277   0.2673691             0.1250000
## 456     1.7043816           8.5     0.4219277   0.2673691             0.1250000
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## 458     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 459     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 460     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 461     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 462     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 463     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 464     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 465     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 466     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 467     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 468     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 469     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 470     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 471     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 472     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 473     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 474     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 475     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 476     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 477     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 478     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 479     1.6299430           6.5     0.8561835   0.7485323             0.0000000
## 480     1.6299430           6.5     0.8561835   0.7485323             0.0000000
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## 482     1.7128054           7.0     0.5498951   0.3792106             0.2857143
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## 485     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 486     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 487     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 488     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 489     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 490     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 491     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 492     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 493     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 494     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 495     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 496     1.7128054           7.0     0.5498951   0.3792106             0.2857143
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## 498     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 499     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 500     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 501     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 502     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 503     1.7128054           7.0     0.5498951   0.3792106             0.2857143
## 504     1.7128054           7.0     0.5498951   0.3792106             0.2857143
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## 508     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 511     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 516     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 519     0.9826256           4.0     0.8091603   0.6794872             0.0000000
## 520     0.9826256           4.0     0.8091603   0.6794872             0.0000000
## 521     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 525     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 528     0.9826256           4.0     0.8091603   0.6794872             0.0000000
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## 552     1.1849855           6.5     0.7669141   0.6219470             0.2000000
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## 556     0.8793394           4.5     0.9340896   0.8763303             0.0000000
## 557     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 561     0.8793394           4.5     0.9340896   0.8763303             0.0000000
## 562     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 565     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 568     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 570     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 573     0.8793394           4.5     0.9340896   0.8763303             0.0000000
## 574     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 576     0.8793394           4.5     0.9340896   0.8763303             0.0000000
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## 579     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 580     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 581     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 582     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 583     2.0784670          11.0     0.0000000   0.0000000             0.0000000
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## 585     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 586     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 587     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 588     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 589     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 590     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 591     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 592     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 593     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 594     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 595     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 596     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 597     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 598     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 599     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 600     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 601     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 602     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 603     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 604     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 605     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 606     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 607     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 608     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 609     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 610     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 611     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 612     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 613     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 614     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 615     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 616     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 617     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 618     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 619     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 620     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 621     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 622     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 623     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 624     1.5325570           7.0     0.6161471   0.4452403             0.2857143
## 625     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 626     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 627     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 628     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 629     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 630     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 631     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 632     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 633     1.5378489           8.0     0.5443405   0.3739477             0.1250000
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## 635     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 636     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 637     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 638     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 639     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 640     1.5378489           8.0     0.5443405   0.3739477             0.1250000
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## 642     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 643     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 644     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 645     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 646     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 647     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 648     1.5378489           8.0     0.5443405   0.3739477             0.1250000
## 649     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 650     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 651     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 652     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 653     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 654     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 655     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 656     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 657     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 658     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 659     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 660     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 661     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 662     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 663     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 664     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 665     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 666     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 667     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 668     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 669     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 670     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 671     1.2717353           5.0     0.8062622   0.6754098             0.0000000
## 672     1.2717353           5.0     0.8062622   0.6754098             0.0000000
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## 674     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 675     1.5711195           6.5     0.5964585   0.4249668             0.5000000
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## 678     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 679     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 680     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 681     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 682     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 683     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 684     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 685     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 686     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 687     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 688     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 689     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 690     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 691     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 692     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 693     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 694     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 695     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 696     1.5711195           6.5     0.5964585   0.4249668             0.5000000
## 697     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 698     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 699     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 700     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 701     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 702     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 703     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 704     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 705     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 706     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 707     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 708     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 709     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 710     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 711     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 712     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 713     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 714     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 715     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 716     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 717     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 718     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 719     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 720     0.9613777           3.5     0.7674121   0.6226024             0.5000000
## 721     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 722     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 723     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 724     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 725     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 726     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 727     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 728     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 729     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 730     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 731     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 732     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 733     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 734     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 735     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 736     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 737     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 738     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 739     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 740     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 741     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 742     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 743     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 744     1.3870793           5.5     0.6086957   0.4375000             0.6000000
## 745     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 746     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 747     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 748     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 749     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 750     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 751     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 752     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 753     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 754     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 755     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 756     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 757     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 758     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 759     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 760     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 761     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 762     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 763     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 764     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 765     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 766     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 767     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 768     0.9302665           3.5     0.6133333   0.4423077             0.0000000
## 769     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 770     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 771     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 772     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 773     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 774     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 775     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 776     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 777     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 778     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 779     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 780     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 781     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 782     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 783     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 784     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 785     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 786     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 787     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 788     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 789     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 790     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 791     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 792     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 793     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 794     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 795     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 796     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 797     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 798     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 799     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 800     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 801     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 802     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 803     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 804     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 805     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 806     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 807     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 808     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 809     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 810     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 811     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 812     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 813     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 814     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 815     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 816     1.7576182           8.0     0.6485050   0.4798427             0.0000000
## 817     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 818     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 819     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 820     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 821     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 822     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 823     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 824     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 825     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 826     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 827     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 828     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 829     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 830     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 831     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 832     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 833     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 834     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 835     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 836     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 837     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 838     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 839     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 840     1.6208829           7.5     0.5405548   0.3703838             0.1428571
## 841     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 842     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 843     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 844     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 845     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 846     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 847     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 848     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 849     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 850     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 851     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 852     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 853     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 854     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 855     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 856     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 857     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 858     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 859     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 860     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 861     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 862     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 863     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 864     1.5861251           6.0     0.8562784   0.7486772             0.0000000
## 865     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 866     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 867     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 868     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 869     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 870     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 871     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 872     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 873     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 874     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 875     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 876     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 877     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 878     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 879     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 880     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 881     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 882     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 883     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 884     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 885     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 886     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 887     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 888     1.6705149           7.5     0.5076640   0.3401808             0.1428571
## 889     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 890     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 891     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 892     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 893     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 894     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 895     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 896     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 897     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 898     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 899     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 900     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 901     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 902     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 903     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 904     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 905     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 906     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 907     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 908     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 909     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 910     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 911     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 912     1.1807457           5.0     0.8075314   0.6771930             0.0000000
## 913     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 914     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 915     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 916     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 917     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 918     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 919     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 920     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 921     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 922     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 923     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 924     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 925     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 926     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 927     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 928     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 929     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 930     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 931     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 932     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 933     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 934     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 935     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 936     1.5071598           6.5     0.8093303   0.6797269             0.2000000
## 937     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 938     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 939     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 940     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 941     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 942     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 943     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 944     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 945     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 946     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 947     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 948     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 949     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 950     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 951     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 952     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 953     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 954     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 955     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 956     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 957     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 958     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 959     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 960     0.9391687           5.0     0.9203269   0.8524125             0.0000000
## 961     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 962     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 963     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 964     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 965     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 966     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 967     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 968     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 969     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 970     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 971     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 972     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 973     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 974     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 975     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 976     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 977     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 978     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 979     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 980     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 981     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 982     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 983     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 984     2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 985     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 986     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 987     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 988     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 989     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 990     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 991     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 992     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 993     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 994     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 995     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 996     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 997     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 998     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 999     1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1000    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1001    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1002    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1003    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1004    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1005    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1006    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1007    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1008    1.5540186           8.0     0.4775502   0.3136722             0.2500000
## 1009    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1010    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1011    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1012    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1013    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1014    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1015    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1016    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1017    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1018    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1019    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1020    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1021    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1022    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1023    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1024    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1025    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1026    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1027    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1028    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1029    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1030    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1031    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1032    1.5213817           8.5     0.6802785   0.5154712             0.1250000
## 1033    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1034    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1035    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1036    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1037    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1038    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1039    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1040    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1041    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1042    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1043    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1044    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1045    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1046    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1047    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1048    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1049    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1050    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1051    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1052    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1053    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1054    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1055    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1056    1.7632593           7.0     0.4519279   0.2919295             0.0000000
## 1057    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1058    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1059    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1060    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1061    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1062    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1063    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1064    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1065    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1066    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1067    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1068    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1069    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1070    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1071    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1072    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1073    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1074    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1075    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1076    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1077    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1078    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1079    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1080    1.3837136           5.5     0.6129142   0.4418719             0.0000000
## 1081    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1082    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1083    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1084    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1085    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1086    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1087    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1088    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1089    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1090    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1091    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1092    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1093    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1094    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1095    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1096    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1097    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1098    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1099    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1100    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1101    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1102    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1103    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1104    1.1906786           4.5     0.9314463   0.8716887             0.3333333
## 1105    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1106    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1107    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1108    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1109    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1110    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1111    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1112    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1113    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1114    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1115    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1116    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1117    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1118    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1119    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1120    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1121    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1122    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1123    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1124    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1125    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1126    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1127    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1128    0.5580146           4.0     0.8940736   0.8084387             0.0000000
## 1129    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1130    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1131    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1132    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1133    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1134    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1135    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1136    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1137    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1138    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1139    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1140    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1141    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1142    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1143    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1144    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1145    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1146    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1147    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1148    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1149    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1150    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1151    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1152    0.6573424           5.0     0.9461440   0.8977925             0.0000000
## 1153    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1154    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1155    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1156    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1157    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1158    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1159    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1160    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1161    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1162    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1163    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1164    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1165    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1166    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1167    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1168    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1169    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1170    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1171    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1172    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1173    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1174    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1175    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1176    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1177    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1178    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1179    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1180    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1181    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1182    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1183    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1184    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1185    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1186    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1187    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1188    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1189    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1190    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1191    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1192    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1193    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1194    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1195    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1196    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1197    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1198    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1199    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1200    1.7881218           8.5     0.4851643   0.3202752             0.1250000
## 1201    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1202    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1203    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1204    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1205    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1206    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1207    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1208    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1209    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1210    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1211    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1212    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1213    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1214    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1215    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1216    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1217    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1218    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1219    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1220    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1221    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1222    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1223    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1224    1.4483385           7.0     0.6127187   0.4416686             0.0000000
## 1225    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1226    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1227    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1228    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1229    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1230    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1231    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1232    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1233    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1234    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1235    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1236    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1237    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1238    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1239    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1240    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1241    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1242    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1243    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1244    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1245    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1246    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1247    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1248    1.6825243           7.0     0.5191233   0.3505514             0.1666667
## 1249    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1250    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1251    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1252    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1253    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1254    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1255    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1256    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1257    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1258    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1259    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1260    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1261    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1262    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1263    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1264    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1265    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1266    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1267    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1268    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1269    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1270    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1271    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1272    1.1569293           4.0     0.7495992   0.5994872             0.3333333
## 1273    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1274    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1275    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1276    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1277    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1278    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1279    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1280    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1281    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1282    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1283    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1284    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1285    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1286    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1287    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1288    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1289    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1290    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1291    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1292    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1293    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1294    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1295    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1296    1.4007946           5.5     0.8509009   0.7404939             0.0000000
## 1297    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1298    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1299    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1300    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1301    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1302    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1303    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1304    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1305    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1306    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1307    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1308    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1309    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1310    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1311    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1312    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1313    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1314    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1315    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1316    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1317    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1318    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1319    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1320    0.6574145           4.0     0.8908058   0.8031107             0.0000000
## 1321    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1322    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1323    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1324    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1325    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1326    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1327    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1328    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1329    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1330    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1331    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1332    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1333    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1334    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1335    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1336    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1337    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1338    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1339    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1340    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1341    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1342    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1343    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1344    0.7227886           4.0     0.9301829   0.8694784             0.0000000
## 1345    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1346    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1347    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1348    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1349    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1350    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1351    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1352    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1353    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1354    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1355    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1356    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1357    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1358    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1359    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1360    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1361    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1362    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1363    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1364    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1365    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1366    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1367    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1368    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1369    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1370    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1371    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1372    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1373    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1374    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1375    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1376    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1377    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1378    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1379    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1380    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1381    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1382    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1383    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1384    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1385    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1386    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1387    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1388    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1389    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1390    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1391    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1392    1.6890637           8.5     0.5151939   0.3469772             0.1250000
## 1393    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1394    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1395    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1396    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1397    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1398    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1399    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1400    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1401    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1402    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1403    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1404    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1405    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1406    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1407    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1408    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1409    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1410    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1411    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1412    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1413    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1414    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1415    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1416    1.6613680           9.0     0.6067317   0.4354738             0.1111111
## 1417    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1418    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1419    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1420    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1421    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1422    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1423    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1424    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1425    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1426    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1427    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1428    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1429    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1430    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1431    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1432    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1433    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1434    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1435    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1436    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1437    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1438    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1439    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1440    1.7836333           7.5     0.5057999   0.3385088             0.0000000
## 1441    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1442    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1443    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1444    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1445    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1446    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1447    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1448    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1449    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1450    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1451    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1452    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1453    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1454    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1455    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1456    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1457    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1458    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1459    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1460    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1461    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1462    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1463    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1464    1.2942730           5.0     0.7453294   0.5940439             0.3333333
## 1465    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1466    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1467    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1468    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1469    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1470    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1471    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1472    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1473    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1474    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1475    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1476    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1477    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1478    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1479    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1480    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1481    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1482    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1483    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1484    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1485    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1486    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1487    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1488    1.2686511           4.5     0.9064490   0.8289043             0.3333333
## 1489    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1490    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1491    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1492    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1493    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1494    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1495    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1496    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1497    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1498    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1499    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1500    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1501    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1502    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1503    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1504    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1505    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1506    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1507    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1508    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1509    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1510    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1511    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1512    0.5707230           4.5     0.8832972   0.7909868             0.0000000
## 1513    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1514    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1515    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1516    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1517    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1518    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1519    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1520    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1521    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1522    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1523    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1524    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1525    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1526    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1527    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1528    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1529    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1530    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1531    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1532    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1533    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1534    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1535    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1536    0.5951247           4.0     0.9576002   0.9186497             0.0000000
## 1537    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1538    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1539    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1540    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1541    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1542    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1543    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1544    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1545    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1546    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1547    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1548    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1549    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1550    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1551    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1552    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1553    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1554    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1555    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1556    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1557    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1558    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1559    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1560    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1561    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1562    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1563    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1564    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1565    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1566    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1567    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1568    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1569    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1570    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1571    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1572    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1573    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1574    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1575    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1576    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1577    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1578    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1579    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1580    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1581    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1582    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1583    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1584    1.5862113           7.5     0.3935303   0.2449659             0.1428571
## 1585    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1586    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1587    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1588    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1589    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1590    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1591    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1592    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1593    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1594    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1595    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1596    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1597    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1598    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1599    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1600    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1601    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1602    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1603    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1604    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1605    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1606    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1607    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1608    1.4948352           8.5     0.6102781   0.4391368             0.1250000
## 1609    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1610    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1611    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1612    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1613    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1614    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1615    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1616    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1617    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1618    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1619    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1620    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1621    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1622    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1623    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1624    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1625    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1626    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1627    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1628    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1629    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1630    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1631    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1632    1.4254256           6.0     0.5077328   0.3402426             0.1666667
## 1633    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1634    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1635    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1636    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1637    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1638    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1639    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1640    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1641    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1642    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1643    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1644    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1645    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1646    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1647    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1648    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1649    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1650    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1651    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1652    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1653    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1654    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1655    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1656    1.1525871           4.5     0.7699053   0.6258911             0.6666667
## 1657    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1658    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1659    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1660    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1661    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1662    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1663    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1664    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1665    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1666    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1667    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1668    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1669    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1670    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1671    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1672    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1673    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1674    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1675    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1676    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1677    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1678    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1679    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1680    1.2474031           4.0     0.9374289   0.8822270             0.6666667
## 1681    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1682    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1683    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1684    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1685    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1686    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1687    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1688    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1689    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1690    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1691    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1692    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1693    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1694    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1695    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1696    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1697    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1698    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1699    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1700    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1701    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1702    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1703    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1704    0.7728167           3.5     0.7388350   0.5858353             0.0000000
## 1705    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1706    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1707    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1708    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1709    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1710    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1711    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1712    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1713    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1714    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1715    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1716    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1717    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1718    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1719    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1720    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1721    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1722    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1723    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1724    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1725    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1726    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1727    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1728    0.6460517           3.0     0.7497872   0.5997277             0.0000000
## 1729    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1730    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1731    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1732    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1733    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1734    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1735    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1736    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1737    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1738    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1739    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1740    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1741    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1742    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1743    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1744    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1745    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1746    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1747    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1748    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1749    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1750    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1751    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1752    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1753    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1754    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1755    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1756    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1757    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1758    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1759    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1760    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1761    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1762    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1763    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1764    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1765    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1766    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1767    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1768    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1769    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1770    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1771    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1772    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1773    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1774    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1775    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1776    1.8112725           8.5     0.5241108   0.3551153             0.0000000
## 1777    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1778    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1779    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1780    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1781    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1782    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1783    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1784    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1785    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1786    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1787    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1788    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1789    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1790    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1791    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1792    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1793    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1794    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1795    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1796    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1797    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1798    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1799    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1800    1.5778693           8.0     0.7006088   0.5391824             0.1428571
## 1801    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1802    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1803    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1804    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1805    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1806    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1807    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1808    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1809    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1810    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1811    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1812    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1813    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1814    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1815    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1816    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1817    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1818    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1819    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1820    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1821    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1822    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1823    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1824    1.7398154           7.0     0.4756098   0.3120000             0.0000000
## 1825    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1826    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1827    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1828    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1829    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1830    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1831    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1832    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1833    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1834    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1835    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1836    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1837    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1838    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1839    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1840    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1841    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1842    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1843    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1844    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1845    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1846    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1847    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1848    1.2519825           5.5     0.7460988   0.5950220             0.3333333
## 1849    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1850    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1851    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1852    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1853    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1854    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1855    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1856    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1857    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1858    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1859    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1860    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1861    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1862    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1863    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1864    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1865    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1866    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1867    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1868    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1869    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1870    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1871    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1872    1.4667712           5.5     0.9079875   0.8314808             0.3333333
## 1873    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1874    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1875    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1876    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1877    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1878    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1879    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1880    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1881    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1882    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1883    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1884    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1885    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1886    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1887    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1888    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1889    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1890    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1891    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1892    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1893    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1894    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1895    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1896    0.8928972           4.5     0.9027828   0.8227931             0.0000000
## 1897    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1898    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1899    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1900    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1901    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1902    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1903    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1904    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1905    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1906    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1907    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1908    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1909    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1910    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1911    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1912    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1913    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1914    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1915    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1916    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1917    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1918    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1919    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1920    0.6549539           4.5     0.9497693   0.9043436             0.0000000
## 1921    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1922    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1923    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1924    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1925    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1926    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1927    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1928    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1929    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1930    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1931    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1932    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1933    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1934    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1935    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1936    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1937    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1938    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1939    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1940    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1941    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1942    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1943    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1944    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 1945    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1946    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1947    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1948    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1949    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1950    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1951    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1952    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1953    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1954    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1955    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1956    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1957    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1958    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1959    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1960    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1961    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1962    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1963    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1964    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1965    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1966    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1967    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1968    1.4587022           7.0     0.4647482   0.3027179             0.0000000
## 1969    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1970    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1971    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1972    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1973    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1974    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1975    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1976    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1977    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1978    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1979    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1980    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1981    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1982    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1983    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1984    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1985    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1986    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1987    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1988    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1989    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1990    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1991    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1992    1.4635307           7.0     0.5222681   0.3534255             0.0000000
## 1993    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1994    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1995    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1996    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1997    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1998    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 1999    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2000    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2001    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2002    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2003    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2004    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2005    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2006    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2007    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2008    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2009    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2010    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2011    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2012    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2013    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2014    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2015    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2016    1.4969088           6.0     0.6928848   0.5300870             0.0000000
## 2017    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2018    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2019    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2020    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2021    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2022    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2023    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2024    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2025    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2026    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2027    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2028    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2029    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2030    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2031    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2032    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2033    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2034    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2035    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2036    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2037    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2038    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2039    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2040    1.4098276           6.0     0.6760997   0.5106878             0.2500000
## 2041    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2042    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2043    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2044    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2045    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2046    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2047    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2048    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2049    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2050    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2051    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2052    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2053    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2054    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2055    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2056    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2057    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2058    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2059    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2060    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2061    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2062    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2063    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2064    0.8677885           4.0     0.8623853   0.7580645             0.5000000
## 2065    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2066    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2067    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2068    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2069    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2070    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2071    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2072    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2073    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2074    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2075    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2076    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2077    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2078    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2079    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2080    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2081    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2082    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2083    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2084    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2085    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2086    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2087    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2088    1.0349319           5.0     0.8940736   0.8084387             0.0000000
## 2089    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2090    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2091    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2092    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2093    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2094    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2095    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2096    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2097    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2098    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2099    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2100    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2101    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2102    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2103    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2104    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2105    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2106    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2107    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2108    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2109    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2110    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2111    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2112    0.8886913           5.5     0.8939828   0.8082902             0.5000000
## 2113    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2114    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2115    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2116    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2117    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2118    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2119    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2120    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2121    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2122    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2123    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2124    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2125    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2126    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2127    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2128    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2129    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2130    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2131    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2132    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2133    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2134    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2135    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2136    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2137    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2138    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2139    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2140    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2141    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2142    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2143    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2144    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2145    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2146    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2147    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2148    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2149    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2150    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2151    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2152    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2153    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2154    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2155    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2156    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2157    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2158    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2159    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2160    1.6928054           7.5     0.5989357   0.4274862             0.1666667
## 2161    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2162    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2163    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2164    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2165    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2166    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2167    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2168    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2169    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2170    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2171    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2172    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2173    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2174    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2175    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2176    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2177    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2178    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2179    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2180    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2181    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2182    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2183    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2184    1.3904874           5.5     0.2931267   0.1717331             0.0000000
## 2185    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2186    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2187    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2188    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2189    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2190    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2191    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2192    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2193    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2194    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2195    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2196    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2197    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2198    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2199    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2200    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2201    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2202    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2203    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2204    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2205    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2206    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2207    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2208    1.4161738           6.0     0.5734298   0.4019640             0.0000000
## 2209    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2210    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2211    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2212    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2213    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2214    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2215    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2216    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2217    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2218    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2219    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2220    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2221    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2222    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2223    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2224    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2225    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2226    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2227    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2228    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2229    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2230    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2231    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2232    1.1830432           4.5     0.5461783   0.3756846             0.2500000
## 2233    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2234    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2235    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2236    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2237    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2238    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2239    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2240    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2241    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2242    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2243    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2244    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2245    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2246    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2247    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2248    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2249    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2250    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2251    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2252    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2253    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2254    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2255    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2256    1.0779045           5.0     0.8424721   0.7278202             0.0000000
## 2257    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2258    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2259    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2260    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2261    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2262    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2263    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2264    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2265    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2266    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2267    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2268    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2269    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2270    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2271    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2272    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2273    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2274    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2275    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2276    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2277    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2278    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2279    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2280    1.1343318           5.0     0.9128705   0.8397072             0.0000000
## 2281    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2282    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2283    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2284    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2285    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2286    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2287    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2288    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2289    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2290    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2291    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2292    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2293    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2294    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2295    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2296    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2297    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2298    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2299    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2300    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2301    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2302    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2303    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2304    0.9541375           4.5     0.8634686   0.7597403             0.5000000
## 2305    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2306    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2307    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2308    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2309    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2310    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2311    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2312    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2313    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2314    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2315    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2316    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2317    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2318    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2319    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2320    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2321    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2322    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2323    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2324    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2325    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2326    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2327    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2328    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2329    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2330    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2331    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2332    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2333    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2334    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2335    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2336    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2337    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2338    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2339    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2340    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2341    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2342    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2343    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2344    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2345    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2346    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2347    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2348    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2349    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2350    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2351    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2352    1.5937474           7.5     0.3858672   0.2390555             0.1666667
## 2353    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2354    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2355    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2356    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2357    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2358    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2359    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2360    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2361    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2362    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2363    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2364    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2365    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2366    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2367    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2368    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2369    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2370    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2371    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2372    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2373    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2374    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2375    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2376    1.6035169           7.5     0.3980177   0.2484533             0.0000000
## 2377    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2378    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2379    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2380    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2381    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2382    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2383    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2384    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2385    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2386    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2387    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2388    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2389    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2390    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2391    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2392    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2393    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2394    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2395    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2396    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2397    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2398    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2399    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2400    1.5172828           6.5     0.6578032   0.4900944             0.0000000
## 2401    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2402    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2403    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2404    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2405    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2406    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2407    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2408    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2409    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2410    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2411    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2412    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2413    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2414    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2415    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2416    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2417    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2418    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2419    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2420    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2421    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2422    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2423    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2424    1.3203870           5.5     0.5871069   0.4155353             0.2500000
## 2425    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2426    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2427    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2428    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2429    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2430    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2431    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2432    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2433    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2434    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2435    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2436    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2437    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2438    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2439    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2440    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2441    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2442    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2443    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2444    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2445    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2446    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2447    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2448    0.9457610           4.0     0.8813489   0.7878675             0.5000000
## 2449    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2450    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2451    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2452    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2453    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2454    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2455    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2456    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2457    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2458    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2459    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2460    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2461    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2462    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2463    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2464    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2465    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2466    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2467    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2468    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2469    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2470    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2471    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2472    1.0476403           5.5     0.8832972   0.7909868             0.0000000
## 2473    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2474    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2475    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2476    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2477    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2478    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2479    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2480    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2481    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2482    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2483    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2484    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2485    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2486    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2487    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2488    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2489    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2490    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2491    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2492    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2493    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2494    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2495    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2496    0.8264735           4.5     0.9161354   0.8452490             0.5000000
## 2497    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2498    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2499    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2500    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2501    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2502    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2503    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2504    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2505    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2506    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2507    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2508    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2509    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2510    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2511    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2512    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2513    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2514    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2515    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2516    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2517    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2518    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2519    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2520    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2521    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2522    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2523    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2524    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2525    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2526    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2527    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2528    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2529    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2530    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2531    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2532    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2533    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2534    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2535    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2536    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2537    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2538    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2539    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2540    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2541    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2542    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2543    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2544    1.4908950           6.5     0.4258033   0.2704892             0.0000000
## 2545    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2546    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2547    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2548    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2549    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2550    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2551    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2552    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2553    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2554    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2555    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2556    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2557    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2558    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2559    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2560    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2561    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2562    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2563    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2564    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2565    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2566    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2567    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2568    1.4369842           7.0     0.5152690   0.3470454             0.0000000
## 2569    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2570    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2571    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2572    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2573    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2574    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2575    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2576    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2577    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2578    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2579    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2580    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2581    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2582    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2583    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2584    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2585    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2586    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2587    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2588    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2589    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2590    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2591    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2592    1.1590750           5.0     0.3094595   0.1830536             0.2500000
## 2593    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2594    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2595    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2596    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2597    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2598    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2599    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2600    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2601    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2602    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2603    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2604    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2605    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2606    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2607    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2608    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2609    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2610    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2611    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2612    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2613    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2614    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2615    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2616    1.1787010           5.0     0.5881557   0.4165868             0.2500000
## 2617    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2618    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2619    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2620    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2621    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2622    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2623    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2624    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2625    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2626    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2627    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2628    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2629    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2630    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2631    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2632    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2633    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2634    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2635    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2636    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2637    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2638    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2639    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2640    0.9245130           3.5     0.8128693   0.6847345             0.5000000
## 2641    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2642    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2643    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2644    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2645    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2646    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2647    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2648    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2649    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2650    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2651    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2652    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2653    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2654    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2655    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2656    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2657    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2658    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2659    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2660    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2661    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2662    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2663    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2664    1.2497340           4.5     0.8109840   0.6820631             0.3333333
## 2665    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2666    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2667    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2668    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2669    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2670    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2671    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2672    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2673    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2674    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2675    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2676    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2677    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2678    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2679    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2680    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2681    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2682    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2683    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2684    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2685    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2686    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2687    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2688    0.8774006           3.5     0.7224848   0.5655391             0.5000000
## 2689    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2690    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2691    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2692    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2693    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2694    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2695    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2696    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2697    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2698    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2699    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2700    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2701    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2702    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2703    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2704    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2705    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2706    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2707    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2708    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2709    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2710    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2711    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2712    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2713    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2714    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2715    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2716    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2717    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2718    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2719    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2720    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2721    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2722    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2723    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2724    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2725    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2726    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2727    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2728    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2729    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2730    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2731    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2732    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2733    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2734    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2735    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2736    1.7159561           7.5     0.6949506   0.5325091             0.0000000
## 2737    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2738    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2739    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2740    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2741    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2742    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2743    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2744    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2745    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2746    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2747    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2748    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2749    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2750    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2751    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2752    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2753    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2754    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2755    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2756    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2757    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2758    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2759    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2760    1.5200182           6.5     0.4408897   0.2827829             0.0000000
## 2761    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2762    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2763    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2764    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2765    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2766    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2767    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2768    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2769    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2770    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2771    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2772    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2773    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2774    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2775    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2776    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2777    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2778    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2779    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2780    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2781    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2782    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2783    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2784    1.4734648           6.0     0.5394322   0.3693305             0.0000000
## 2785    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2786    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2787    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2788    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2789    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2790    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2791    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2792    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2793    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2794    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2795    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2796    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2797    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2798    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2799    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2800    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2801    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2802    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2803    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2804    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2805    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2806    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2807    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2808    1.2780964           6.0     0.5398773   0.3697479             0.2500000
## 2809    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2810    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2811    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2812    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2813    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2814    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2815    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2816    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2817    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2818    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2819    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2820    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2821    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2822    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2823    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2824    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2825    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2826    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2827    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2828    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2829    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2830    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2831    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2832    1.1438811           5.0     0.8833879   0.7911323             0.5000000
## 2833    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2834    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2835    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2836    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2837    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2838    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2839    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2840    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2841    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2842    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2843    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2844    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2845    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2846    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2847    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2848    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2849    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2850    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2851    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2852    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2853    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2854    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2855    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2856    1.3698145           5.5     0.9027828   0.8227931             0.0000000
## 2857    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2858    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2859    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2860    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2861    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2862    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2863    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2864    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2865    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2866    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2867    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2868    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2869    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2870    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2871    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2872    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2873    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2874    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2875    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2876    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2877    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2878    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2879    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2880    0.8863028           5.0     0.9009702   0.8197869             0.5000000
## 2881    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2882    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2883    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2884    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2885    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2886    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2887    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2888    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2889    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2890    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2891    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2892    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2893    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2894    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2895    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2896    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2897    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2898    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2899    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2900    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2901    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2902    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2903    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2904    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 2905    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2906    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2907    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2908    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2909    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2910    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2911    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2912    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2913    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2914    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2915    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2916    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2917    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2918    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2919    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2920    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2921    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2922    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2923    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2924    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2925    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2926    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2927    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2928    1.4093629           7.5     0.3571855   0.2174229             0.1428571
## 2929    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2930    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2931    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2932    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2933    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2934    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2935    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2936    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2937    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2938    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2939    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2940    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2941    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2942    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2943    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2944    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2945    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2946    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2947    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2948    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2949    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2950    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2951    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2952    1.5566020           8.0     0.4330945   0.2764012             0.2500000
## 2953    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2954    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2955    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2956    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2957    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2958    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2959    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2960    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2961    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2962    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2963    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2964    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2965    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2966    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2967    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2968    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2969    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2970    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2971    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2972    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2973    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2974    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2975    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2976    1.3271205           5.0     0.7095691   0.5498699             0.0000000
## 2977    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2978    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2979    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2980    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2981    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2982    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2983    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2984    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2985    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2986    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2987    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2988    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2989    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2990    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2991    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2992    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2993    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2994    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2995    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2996    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2997    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2998    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 2999    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 3000    1.4700682           6.0     0.5603006   0.3891789             0.0000000
## 3001    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3002    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3003    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3004    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3005    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3006    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3007    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3008    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3009    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3010    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3011    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3012    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3013    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3014    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3015    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3016    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3017    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3018    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3019    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3020    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3021    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3022    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3023    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3024    1.0940609           4.5     0.8165592   0.6899874             0.0000000
## 3025    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3026    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3027    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3028    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3029    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3030    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3031    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3032    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3033    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3034    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3035    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3036    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3037    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3038    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3039    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3040    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3041    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3042    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3043    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3044    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3045    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3046    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3047    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3048    0.8851120           4.5     0.9186702   0.8495744             0.5000000
## 3049    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3050    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3051    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3052    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3053    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3054    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3055    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3056    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3057    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3058    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3059    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3060    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3061    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3062    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3063    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3064    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3065    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3066    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3067    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3068    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3069    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3070    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3071    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3072    1.3912750           7.0     0.8836783   0.7915983             0.0000000
## 3073    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3074    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3075    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3076    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3077    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3078    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3079    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3080    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3081    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3082    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3083    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3084    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3085    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3086    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3087    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3088    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3089    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3090    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3091    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3092    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3093    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3094    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3095    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3096    2.0784670          11.0     0.0000000   0.0000000             0.0000000
## 3097    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3098    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3099    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3100    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3101    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3102    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3103    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3104    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3105    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3106    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3107    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3108    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3109    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3110    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3111    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3112    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3113    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3114    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3115    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3116    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3117    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3118    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3119    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3120    1.6434662           8.0     0.6412607   0.4719528             0.1428571
## 3121    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3122    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3123    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3124    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3125    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3126    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3127    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3128    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3129    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3130    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3131    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3132    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3133    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3134    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3135    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3136    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3137    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3138    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3139    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3140    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3141    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3142    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3143    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3144    1.4835587           6.5     0.3623978   0.2212978             0.0000000
## 3145    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3146    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3147    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3148    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3149    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3150    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3151    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3152    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3153    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3154    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3155    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3156    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3157    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3158    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3159    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3160    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3161    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3162    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3163    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3164    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3165    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3166    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3167    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3168    1.2463855           5.0     0.7793072   0.6384138             0.0000000
## 3169    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3170    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3171    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3172    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3173    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3174    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3175    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3176    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3177    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3178    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3179    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3180    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3181    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3182    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3183    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3184    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3185    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3186    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3187    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3188    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3189    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3190    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3191    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3192    1.2432838           4.5     0.6892984   0.5259003             0.2500000
## 3193    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3194    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3195    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3196    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3197    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3198    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3199    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3200    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3201    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3202    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3203    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3204    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3205    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3206    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3207    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3208    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3209    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3210    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3211    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3212    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3213    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3214    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3215    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3216    1.3041770           5.5     0.8280669   0.7065821             0.0000000
## 3217    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3218    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3219    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3220    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3221    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3222    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3223    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3224    0.9845120           4.5     0.8706428   0.7709188             0.0000000
## 3225    0.9845120           4.5     0.8706428   0.7709188             0.0000000
##      beta_nestedness beta_total metaecosystem_richness
## 1         0.00000000 0.00000000                     11
## 2         0.00000000 0.00000000                     11
## 3         0.00000000 0.00000000                     11
## 4         0.00000000 0.00000000                     11
## 5         0.00000000 0.00000000                     11
## 6         0.00000000 0.00000000                     11
## 7         0.00000000 0.00000000                     11
## 8         0.00000000 0.00000000                     11
## 9         0.00000000 0.00000000                     11
## 10        0.00000000 0.00000000                     11
## 11        0.00000000 0.00000000                     11
## 12        0.00000000 0.00000000                     11
## 13        0.00000000 0.00000000                     11
## 14        0.00000000 0.00000000                     11
## 15        0.00000000 0.00000000                     11
## 16        0.00000000 0.00000000                     11
## 17        0.00000000 0.00000000                     11
## 18        0.00000000 0.00000000                     11
## 19        0.00000000 0.00000000                     11
## 20        0.00000000 0.00000000                     11
## 21        0.00000000 0.00000000                     11
## 22        0.00000000 0.00000000                     11
## 23        0.00000000 0.00000000                     11
## 24        0.00000000 0.00000000                     11
## 25        0.05714286 0.20000000                      9
## 26        0.05714286 0.20000000                      9
## 27        0.05714286 0.20000000                      9
## 28        0.05714286 0.20000000                      9
## 29        0.05714286 0.20000000                      9
## 30        0.05714286 0.20000000                      9
## 31        0.05714286 0.20000000                      9
## 32        0.05714286 0.20000000                      9
## 33        0.05714286 0.20000000                      9
## 34        0.05714286 0.20000000                      9
## 35        0.05714286 0.20000000                      9
## 36        0.05714286 0.20000000                      9
## 37        0.05714286 0.20000000                      9
## 38        0.05714286 0.20000000                      9
## 39        0.05714286 0.20000000                      9
## 40        0.05714286 0.20000000                      9
## 41        0.05714286 0.20000000                      9
## 42        0.05714286 0.20000000                      9
## 43        0.05714286 0.20000000                      9
## 44        0.05714286 0.20000000                      9
## 45        0.05714286 0.20000000                      9
## 46        0.05714286 0.20000000                      9
## 47        0.05714286 0.20000000                      9
## 48        0.05714286 0.20000000                      9
## 49        0.00000000 0.12500000                      9
## 50        0.00000000 0.12500000                      9
## 51        0.00000000 0.12500000                      9
## 52        0.00000000 0.12500000                      9
## 53        0.00000000 0.12500000                      9
## 54        0.00000000 0.12500000                      9
## 55        0.00000000 0.12500000                      9
## 56        0.00000000 0.12500000                      9
## 57        0.00000000 0.12500000                      9
## 58        0.00000000 0.12500000                      9
## 59        0.00000000 0.12500000                      9
## 60        0.00000000 0.12500000                      9
## 61        0.00000000 0.12500000                      9
## 62        0.00000000 0.12500000                      9
## 63        0.00000000 0.12500000                      9
## 64        0.00000000 0.12500000                      9
## 65        0.00000000 0.12500000                      9
## 66        0.00000000 0.12500000                      9
## 67        0.00000000 0.12500000                      9
## 68        0.00000000 0.12500000                      9
## 69        0.00000000 0.12500000                      9
## 70        0.00000000 0.12500000                      9
## 71        0.00000000 0.12500000                      9
## 72        0.00000000 0.12500000                      9
## 73        0.33333333 0.33333333                      8
## 74        0.33333333 0.33333333                      8
## 75        0.33333333 0.33333333                      8
## 76        0.33333333 0.33333333                      8
## 77        0.33333333 0.33333333                      8
## 78        0.33333333 0.33333333                      8
## 79        0.33333333 0.33333333                      8
## 80        0.33333333 0.33333333                      8
## 81        0.33333333 0.33333333                      8
## 82        0.33333333 0.33333333                      8
## 83        0.33333333 0.33333333                      8
## 84        0.33333333 0.33333333                      8
## 85        0.33333333 0.33333333                      8
## 86        0.33333333 0.33333333                      8
## 87        0.33333333 0.33333333                      8
## 88        0.33333333 0.33333333                      8
## 89        0.33333333 0.33333333                      8
## 90        0.33333333 0.33333333                      8
## 91        0.33333333 0.33333333                      8
## 92        0.33333333 0.33333333                      8
## 93        0.33333333 0.33333333                      8
## 94        0.33333333 0.33333333                      8
## 95        0.33333333 0.33333333                      8
## 96        0.33333333 0.33333333                      8
## 97        0.05714286 0.20000000                      9
## 98        0.05714286 0.20000000                      9
## 99        0.05714286 0.20000000                      9
## 100       0.05714286 0.20000000                      9
## 101       0.05714286 0.20000000                      9
## 102       0.05714286 0.20000000                      9
## 103       0.05714286 0.20000000                      9
## 104       0.05714286 0.20000000                      9
## 105       0.05714286 0.20000000                      9
## 106       0.05714286 0.20000000                      9
## 107       0.05714286 0.20000000                      9
## 108       0.05714286 0.20000000                      9
## 109       0.05714286 0.20000000                      9
## 110       0.05714286 0.20000000                      9
## 111       0.05714286 0.20000000                      9
## 112       0.05714286 0.20000000                      9
## 113       0.05714286 0.20000000                      9
## 114       0.05714286 0.20000000                      9
## 115       0.05714286 0.20000000                      9
## 116       0.05714286 0.20000000                      9
## 117       0.05714286 0.20000000                      9
## 118       0.05714286 0.20000000                      9
## 119       0.05714286 0.20000000                      9
## 120       0.05714286 0.20000000                      9
## 121       0.50000000 0.50000000                      6
## 122       0.50000000 0.50000000                      6
## 123       0.50000000 0.50000000                      6
## 124       0.50000000 0.50000000                      6
## 125       0.50000000 0.50000000                      6
## 126       0.50000000 0.50000000                      6
## 127       0.50000000 0.50000000                      6
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## 3025      0.27777778 0.77777778                      8
## 3026      0.27777778 0.77777778                      8
## 3027      0.27777778 0.77777778                      8
## 3028      0.27777778 0.77777778                      8
## 3029      0.27777778 0.77777778                      8
## 3030      0.27777778 0.77777778                      8
## 3031      0.27777778 0.77777778                      8
## 3032      0.27777778 0.77777778                      8
## 3033      0.27777778 0.77777778                      8
## 3034      0.27777778 0.77777778                      8
## 3035      0.27777778 0.77777778                      8
## 3036      0.27777778 0.77777778                      8
## 3037      0.27777778 0.77777778                      8
## 3038      0.27777778 0.77777778                      8
## 3039      0.27777778 0.77777778                      8
## 3040      0.27777778 0.77777778                      8
## 3041      0.27777778 0.77777778                      8
## 3042      0.27777778 0.77777778                      8
## 3043      0.27777778 0.77777778                      8
## 3044      0.27777778 0.77777778                      8
## 3045      0.27777778 0.77777778                      8
## 3046      0.27777778 0.77777778                      8
## 3047      0.27777778 0.77777778                      8
## 3048      0.27777778 0.77777778                      8
## 3049      0.28571429 0.28571429                      9
## 3050      0.28571429 0.28571429                      9
## 3051      0.28571429 0.28571429                      9
## 3052      0.28571429 0.28571429                      9
## 3053      0.28571429 0.28571429                      9
## 3054      0.28571429 0.28571429                      9
## 3055      0.28571429 0.28571429                      9
## 3056      0.28571429 0.28571429                      9
## 3057      0.28571429 0.28571429                      9
## 3058      0.28571429 0.28571429                      9
## 3059      0.28571429 0.28571429                      9
## 3060      0.28571429 0.28571429                      9
## 3061      0.28571429 0.28571429                      9
## 3062      0.28571429 0.28571429                      9
## 3063      0.28571429 0.28571429                      9
## 3064      0.28571429 0.28571429                      9
## 3065      0.28571429 0.28571429                      9
## 3066      0.28571429 0.28571429                      9
## 3067      0.28571429 0.28571429                      9
## 3068      0.28571429 0.28571429                      9
## 3069      0.28571429 0.28571429                      9
## 3070      0.28571429 0.28571429                      9
## 3071      0.28571429 0.28571429                      9
## 3072      0.28571429 0.28571429                      9
## 3073      0.00000000 0.00000000                     11
## 3074      0.00000000 0.00000000                     11
## 3075      0.00000000 0.00000000                     11
## 3076      0.00000000 0.00000000                     11
## 3077      0.00000000 0.00000000                     11
## 3078      0.00000000 0.00000000                     11
## 3079      0.00000000 0.00000000                     11
## 3080      0.00000000 0.00000000                     11
## 3081      0.00000000 0.00000000                     11
## 3082      0.00000000 0.00000000                     11
## 3083      0.00000000 0.00000000                     11
## 3084      0.00000000 0.00000000                     11
## 3085      0.00000000 0.00000000                     11
## 3086      0.00000000 0.00000000                     11
## 3087      0.00000000 0.00000000                     11
## 3088      0.00000000 0.00000000                     11
## 3089      0.00000000 0.00000000                     11
## 3090      0.00000000 0.00000000                     11
## 3091      0.00000000 0.00000000                     11
## 3092      0.00000000 0.00000000                     11
## 3093      0.00000000 0.00000000                     11
## 3094      0.00000000 0.00000000                     11
## 3095      0.00000000 0.00000000                     11
## 3096      0.00000000 0.00000000                     11
## 3097      0.10714286 0.25000000                     10
## 3098      0.10714286 0.25000000                     10
## 3099      0.10714286 0.25000000                     10
## 3100      0.10714286 0.25000000                     10
## 3101      0.10714286 0.25000000                     10
## 3102      0.10714286 0.25000000                     10
## 3103      0.10714286 0.25000000                     10
## 3104      0.10714286 0.25000000                     10
## 3105      0.10714286 0.25000000                     10
## 3106      0.10714286 0.25000000                     10
## 3107      0.10714286 0.25000000                     10
## 3108      0.10714286 0.25000000                     10
## 3109      0.10714286 0.25000000                     10
## 3110      0.10714286 0.25000000                     10
## 3111      0.10714286 0.25000000                     10
## 3112      0.10714286 0.25000000                     10
## 3113      0.10714286 0.25000000                     10
## 3114      0.10714286 0.25000000                     10
## 3115      0.10714286 0.25000000                     10
## 3116      0.10714286 0.25000000                     10
## 3117      0.10714286 0.25000000                     10
## 3118      0.10714286 0.25000000                     10
## 3119      0.10714286 0.25000000                     10
## 3120      0.10714286 0.25000000                     10
## 3121      0.23076923 0.23076923                      8
## 3122      0.23076923 0.23076923                      8
## 3123      0.23076923 0.23076923                      8
## 3124      0.23076923 0.23076923                      8
## 3125      0.23076923 0.23076923                      8
## 3126      0.23076923 0.23076923                      8
## 3127      0.23076923 0.23076923                      8
## 3128      0.23076923 0.23076923                      8
## 3129      0.23076923 0.23076923                      8
## 3130      0.23076923 0.23076923                      8
## 3131      0.23076923 0.23076923                      8
## 3132      0.23076923 0.23076923                      8
## 3133      0.23076923 0.23076923                      8
## 3134      0.23076923 0.23076923                      8
## 3135      0.23076923 0.23076923                      8
## 3136      0.23076923 0.23076923                      8
## 3137      0.23076923 0.23076923                      8
## 3138      0.23076923 0.23076923                      8
## 3139      0.23076923 0.23076923                      8
## 3140      0.23076923 0.23076923                      8
## 3141      0.23076923 0.23076923                      8
## 3142      0.23076923 0.23076923                      8
## 3143      0.23076923 0.23076923                      8
## 3144      0.23076923 0.23076923                      8
## 3145      0.60000000 0.60000000                      8
## 3146      0.60000000 0.60000000                      8
## 3147      0.60000000 0.60000000                      8
## 3148      0.60000000 0.60000000                      8
## 3149      0.60000000 0.60000000                      8
## 3150      0.60000000 0.60000000                      8
## 3151      0.60000000 0.60000000                      8
## 3152      0.60000000 0.60000000                      8
## 3153      0.60000000 0.60000000                      8
## 3154      0.60000000 0.60000000                      8
## 3155      0.60000000 0.60000000                      8
## 3156      0.60000000 0.60000000                      8
## 3157      0.60000000 0.60000000                      8
## 3158      0.60000000 0.60000000                      8
## 3159      0.60000000 0.60000000                      8
## 3160      0.60000000 0.60000000                      8
## 3161      0.60000000 0.60000000                      8
## 3162      0.60000000 0.60000000                      8
## 3163      0.60000000 0.60000000                      8
## 3164      0.60000000 0.60000000                      8
## 3165      0.60000000 0.60000000                      8
## 3166      0.60000000 0.60000000                      8
## 3167      0.60000000 0.60000000                      8
## 3168      0.60000000 0.60000000                      8
## 3169      0.08333333 0.33333333                      6
## 3170      0.08333333 0.33333333                      6
## 3171      0.08333333 0.33333333                      6
## 3172      0.08333333 0.33333333                      6
## 3173      0.08333333 0.33333333                      6
## 3174      0.08333333 0.33333333                      6
## 3175      0.08333333 0.33333333                      6
## 3176      0.08333333 0.33333333                      6
## 3177      0.08333333 0.33333333                      6
## 3178      0.08333333 0.33333333                      6
## 3179      0.08333333 0.33333333                      6
## 3180      0.08333333 0.33333333                      6
## 3181      0.08333333 0.33333333                      6
## 3182      0.08333333 0.33333333                      6
## 3183      0.08333333 0.33333333                      6
## 3184      0.08333333 0.33333333                      6
## 3185      0.08333333 0.33333333                      6
## 3186      0.08333333 0.33333333                      6
## 3187      0.08333333 0.33333333                      6
## 3188      0.08333333 0.33333333                      6
## 3189      0.08333333 0.33333333                      6
## 3190      0.08333333 0.33333333                      6
## 3191      0.08333333 0.33333333                      6
## 3192      0.08333333 0.33333333                      6
## 3193      0.45454545 0.45454545                      8
## 3194      0.45454545 0.45454545                      8
## 3195      0.45454545 0.45454545                      8
## 3196      0.45454545 0.45454545                      8
## 3197      0.45454545 0.45454545                      8
## 3198      0.45454545 0.45454545                      8
## 3199      0.45454545 0.45454545                      8
## 3200      0.45454545 0.45454545                      8
## 3201      0.45454545 0.45454545                      8
## 3202      0.45454545 0.45454545                      8
## 3203      0.45454545 0.45454545                      8
## 3204      0.45454545 0.45454545                      8
## 3205      0.45454545 0.45454545                      8
## 3206      0.45454545 0.45454545                      8
## 3207      0.45454545 0.45454545                      8
## 3208      0.45454545 0.45454545                      8
## 3209      0.45454545 0.45454545                      8
## 3210      0.45454545 0.45454545                      8
## 3211      0.45454545 0.45454545                      8
## 3212      0.45454545 0.45454545                      8
## 3213      0.45454545 0.45454545                      8
## 3214      0.45454545 0.45454545                      8
## 3215      0.45454545 0.45454545                      8
## 3216      0.45454545 0.45454545                      8
## 3217      0.55555556 0.55555556                      7
## 3218      0.55555556 0.55555556                      7
## 3219      0.55555556 0.55555556                      7
## 3220      0.55555556 0.55555556                      7
## 3221      0.55555556 0.55555556                      7
## 3222      0.55555556 0.55555556                      7
## 3223      0.55555556 0.55555556                      7
## 3224      0.55555556 0.55555556                      7
## 3225      0.55555556 0.55555556                      7
##      total_metaecosystem_bioarea_mm2 total_metaecosystem_Ble_indiv
## 1                           96.92197                      217.1512
## 2                           96.92197                      217.1512
## 3                           96.92197                      217.1512
## 4                           96.92197                      217.1512
## 5                           96.92197                      217.1512
## 6                           96.92197                      217.1512
## 7                           96.92197                      217.1512
## 8                           96.92197                      217.1512
## 9                           96.92197                      217.1512
## 10                          96.92197                      217.1512
## 11                          96.92197                      217.1512
## 12                          96.92197                      217.1512
## 13                          96.92197                      217.1512
## 14                          96.92197                      217.1512
## 15                          96.92197                      217.1512
## 16                          96.92197                      217.1512
## 17                          96.92197                      217.1512
## 18                          96.92197                      217.1512
## 19                          96.92197                      217.1512
## 20                          96.92197                      217.1512
## 21                          96.92197                      217.1512
## 22                          96.92197                      217.1512
## 23                          96.92197                      217.1512
## 24                          96.92197                      217.1512
## 25                         152.90836                     1037.7907
## 26                         152.90836                     1037.7907
## 27                         152.90836                     1037.7907
## 28                         152.90836                     1037.7907
## 29                         152.90836                     1037.7907
## 30                         152.90836                     1037.7907
## 31                         152.90836                     1037.7907
## 32                         152.90836                     1037.7907
## 33                         152.90836                     1037.7907
## 34                         152.90836                     1037.7907
## 35                         152.90836                     1037.7907
## 36                         152.90836                     1037.7907
## 37                         152.90836                     1037.7907
## 38                         152.90836                     1037.7907
## 39                         152.90836                     1037.7907
## 40                         152.90836                     1037.7907
## 41                         152.90836                     1037.7907
## 42                         152.90836                     1037.7907
## 43                         152.90836                     1037.7907
## 44                         152.90836                     1037.7907
## 45                         152.90836                     1037.7907
## 46                         152.90836                     1037.7907
## 47                         152.90836                     1037.7907
## 48                         152.90836                     1037.7907
## 49                         163.09305                     2398.2558
## 50                         163.09305                     2398.2558
## 51                         163.09305                     2398.2558
## 52                         163.09305                     2398.2558
## 53                         163.09305                     2398.2558
## 54                         163.09305                     2398.2558
## 55                         163.09305                     2398.2558
## 56                         163.09305                     2398.2558
## 57                         163.09305                     2398.2558
## 58                         163.09305                     2398.2558
## 59                         163.09305                     2398.2558
## 60                         163.09305                     2398.2558
## 61                         163.09305                     2398.2558
## 62                         163.09305                     2398.2558
## 63                         163.09305                     2398.2558
## 64                         163.09305                     2398.2558
## 65                         163.09305                     2398.2558
## 66                         163.09305                     2398.2558
## 67                         163.09305                     2398.2558
## 68                         163.09305                     2398.2558
## 69                         163.09305                     2398.2558
## 70                         163.09305                     2398.2558
## 71                         163.09305                     2398.2558
## 72                         163.09305                     2398.2558
## 73                         182.03030                     3174.4186
## 74                         182.03030                     3174.4186
## 75                         182.03030                     3174.4186
## 76                         182.03030                     3174.4186
## 77                         182.03030                     3174.4186
## 78                         182.03030                     3174.4186
## 79                         182.03030                     3174.4186
## 80                         182.03030                     3174.4186
## 81                         182.03030                     3174.4186
## 82                         182.03030                     3174.4186
## 83                         182.03030                     3174.4186
## 84                         182.03030                     3174.4186
## 85                         182.03030                     3174.4186
## 86                         182.03030                     3174.4186
## 87                         182.03030                     3174.4186
## 88                         182.03030                     3174.4186
## 89                         182.03030                     3174.4186
## 90                         182.03030                     3174.4186
## 91                         182.03030                     3174.4186
## 92                         182.03030                     3174.4186
## 93                         182.03030                     3174.4186
## 94                         182.03030                     3174.4186
## 95                         182.03030                     3174.4186
## 96                         182.03030                     3174.4186
## 97                         140.28580                     4006.3953
## 98                         140.28580                     4006.3953
## 99                         140.28580                     4006.3953
## 100                        140.28580                     4006.3953
## 101                        140.28580                     4006.3953
## 102                        140.28580                     4006.3953
## 103                        140.28580                     4006.3953
## 104                        140.28580                     4006.3953
## 105                        140.28580                     4006.3953
## 106                        140.28580                     4006.3953
## 107                        140.28580                     4006.3953
## 108                        140.28580                     4006.3953
## 109                        140.28580                     4006.3953
## 110                        140.28580                     4006.3953
## 111                        140.28580                     4006.3953
## 112                        140.28580                     4006.3953
## 113                        140.28580                     4006.3953
## 114                        140.28580                     4006.3953
## 115                        140.28580                     4006.3953
## 116                        140.28580                     4006.3953
## 117                        140.28580                     4006.3953
## 118                        140.28580                     4006.3953
## 119                        140.28580                     4006.3953
## 120                        140.28580                     4006.3953
## 121                        117.28324                     3750.0000
## 122                        117.28324                     3750.0000
## 123                        117.28324                     3750.0000
## 124                        117.28324                     3750.0000
## 125                        117.28324                     3750.0000
## 126                        117.28324                     3750.0000
## 127                        117.28324                     3750.0000
## 128                        117.28324                     3750.0000
## 129                        117.28324                     3750.0000
## 130                        117.28324                     3750.0000
## 131                        117.28324                     3750.0000
## 132                        117.28324                     3750.0000
## 133                        117.28324                     3750.0000
## 134                        117.28324                     3750.0000
## 135                        117.28324                     3750.0000
## 136                        117.28324                     3750.0000
## 137                        117.28324                     3750.0000
## 138                        117.28324                     3750.0000
## 139                        117.28324                     3750.0000
## 140                        117.28324                     3750.0000
## 141                        117.28324                     3750.0000
## 142                        117.28324                     3750.0000
## 143                        117.28324                     3750.0000
## 144                        117.28324                     3750.0000
## 145                        103.80974                     2986.9186
## 146                        103.80974                     2986.9186
## 147                        103.80974                     2986.9186
## 148                        103.80974                     2986.9186
## 149                        103.80974                     2986.9186
## 150                        103.80974                     2986.9186
## 151                        103.80974                     2986.9186
## 152                        103.80974                     2986.9186
## 153                        103.80974                     2986.9186
## 154                        103.80974                     2986.9186
## 155                        103.80974                     2986.9186
## 156                        103.80974                     2986.9186
## 157                        103.80974                     2986.9186
## 158                        103.80974                     2986.9186
## 159                        103.80974                     2986.9186
## 160                        103.80974                     2986.9186
## 161                        103.80974                     2986.9186
## 162                        103.80974                     2986.9186
## 163                        103.80974                     2986.9186
## 164                        103.80974                     2986.9186
## 165                        103.80974                     2986.9186
## 166                        103.80974                     2986.9186
## 167                        103.80974                     2986.9186
## 168                        103.80974                     2986.9186
## 169                         92.60172                     1748.5465
## 170                         92.60172                     1748.5465
## 171                         92.60172                     1748.5465
## 172                         92.60172                     1748.5465
## 173                         92.60172                     1748.5465
## 174                         92.60172                     1748.5465
## 175                         92.60172                     1748.5465
## 176                         92.60172                     1748.5465
## 177                         92.60172                     1748.5465
## 178                         92.60172                     1748.5465
## 179                         92.60172                     1748.5465
## 180                         92.60172                     1748.5465
## 181                         92.60172                     1748.5465
## 182                         92.60172                     1748.5465
## 183                         92.60172                     1748.5465
## 184                         92.60172                     1748.5465
## 185                         92.60172                     1748.5465
## 186                         92.60172                     1748.5465
## 187                         92.60172                     1748.5465
## 188                         92.60172                     1748.5465
## 189                         92.60172                     1748.5465
## 190                         92.60172                     1748.5465
## 191                         92.60172                     1748.5465
## 192                         92.60172                     1748.5465
## 193                         96.92197                      217.1512
## 194                         96.92197                      217.1512
## 195                         96.92197                      217.1512
## 196                         96.92197                      217.1512
## 197                         96.92197                      217.1512
## 198                         96.92197                      217.1512
## 199                         96.92197                      217.1512
## 200                         96.92197                      217.1512
## 201                         96.92197                      217.1512
## 202                         96.92197                      217.1512
## 203                         96.92197                      217.1512
## 204                         96.92197                      217.1512
## 205                         96.92197                      217.1512
## 206                         96.92197                      217.1512
## 207                         96.92197                      217.1512
## 208                         96.92197                      217.1512
## 209                         96.92197                      217.1512
## 210                         96.92197                      217.1512
## 211                         96.92197                      217.1512
## 212                         96.92197                      217.1512
## 213                         96.92197                      217.1512
## 214                         96.92197                      217.1512
## 215                         96.92197                      217.1512
## 216                         96.92197                      217.1512
## 217                        229.78020                     5537.7907
## 218                        229.78020                     5537.7907
## 219                        229.78020                     5537.7907
## 220                        229.78020                     5537.7907
## 221                        229.78020                     5537.7907
## 222                        229.78020                     5537.7907
## 223                        229.78020                     5537.7907
## 224                        229.78020                     5537.7907
## 225                        229.78020                     5537.7907
## 226                        229.78020                     5537.7907
## 227                        229.78020                     5537.7907
## 228                        229.78020                     5537.7907
## 229                        229.78020                     5537.7907
## 230                        229.78020                     5537.7907
## 231                        229.78020                     5537.7907
## 232                        229.78020                     5537.7907
## 233                        229.78020                     5537.7907
## 234                        229.78020                     5537.7907
## 235                        229.78020                     5537.7907
## 236                        229.78020                     5537.7907
## 237                        229.78020                     5537.7907
## 238                        229.78020                     5537.7907
## 239                        229.78020                     5537.7907
## 240                        229.78020                     5537.7907
## 241                        133.25628                     2904.0698
## 242                        133.25628                     2904.0698
## 243                        133.25628                     2904.0698
## 244                        133.25628                     2904.0698
## 245                        133.25628                     2904.0698
## 246                        133.25628                     2904.0698
## 247                        133.25628                     2904.0698
## 248                        133.25628                     2904.0698
## 249                        133.25628                     2904.0698
## 250                        133.25628                     2904.0698
## 251                        133.25628                     2904.0698
## 252                        133.25628                     2904.0698
## 253                        133.25628                     2904.0698
## 254                        133.25628                     2904.0698
## 255                        133.25628                     2904.0698
## 256                        133.25628                     2904.0698
## 257                        133.25628                     2904.0698
## 258                        133.25628                     2904.0698
## 259                        133.25628                     2904.0698
## 260                        133.25628                     2904.0698
## 261                        133.25628                     2904.0698
## 262                        133.25628                     2904.0698
## 263                        133.25628                     2904.0698
## 264                        133.25628                     2904.0698
## 265                        209.35334                     4752.9070
## 266                        209.35334                     4752.9070
## 267                        209.35334                     4752.9070
## 268                        209.35334                     4752.9070
## 269                        209.35334                     4752.9070
## 270                        209.35334                     4752.9070
## 271                        209.35334                     4752.9070
## 272                        209.35334                     4752.9070
## 273                        209.35334                     4752.9070
## 274                        209.35334                     4752.9070
## 275                        209.35334                     4752.9070
## 276                        209.35334                     4752.9070
## 277                        209.35334                     4752.9070
## 278                        209.35334                     4752.9070
## 279                        209.35334                     4752.9070
## 280                        209.35334                     4752.9070
## 281                        209.35334                     4752.9070
## 282                        209.35334                     4752.9070
## 283                        209.35334                     4752.9070
## 284                        209.35334                     4752.9070
## 285                        209.35334                     4752.9070
## 286                        209.35334                     4752.9070
## 287                        209.35334                     4752.9070
## 288                        209.35334                     4752.9070
## 289                        176.95269                     3430.8140
## 290                        176.95269                     3430.8140
## 291                        176.95269                     3430.8140
## 292                        176.95269                     3430.8140
## 293                        176.95269                     3430.8140
## 294                        176.95269                     3430.8140
## 295                        176.95269                     3430.8140
## 296                        176.95269                     3430.8140
## 297                        176.95269                     3430.8140
## 298                        176.95269                     3430.8140
## 299                        176.95269                     3430.8140
## 300                        176.95269                     3430.8140
## 301                        176.95269                     3430.8140
## 302                        176.95269                     3430.8140
## 303                        176.95269                     3430.8140
## 304                        176.95269                     3430.8140
## 305                        176.95269                     3430.8140
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## 839                        154.62321                     2398.2558
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## 864                        190.63761                     2398.2558
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## 888                        214.36970                     1512.2093
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## 912                        171.23111                     2162.7907
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## 934                        151.16653                     4547.9651
## 935                        151.16653                     4547.9651
## 936                        151.16653                     4547.9651
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## 958                        111.09645                     3920.0581
## 959                        111.09645                     3920.0581
## 960                        111.09645                     3920.0581
## 961                         96.92197                      217.1512
## 962                         96.92197                      217.1512
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## 980                         96.92197                      217.1512
## 981                         96.92197                      217.1512
## 982                         96.92197                      217.1512
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## 984                         96.92197                      217.1512
## 985                        147.30148                      819.7674
## 986                        147.30148                      819.7674
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## 1008                       147.30148                      819.7674
## 1009                       179.42053                     2886.6279
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## 1030                       179.42053                     2886.6279
## 1031                       179.42053                     2886.6279
## 1032                       179.42053                     2886.6279
## 1033                       198.43677                     3392.4419
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## 1056                       198.43677                     3392.4419
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## 1080                       130.67795                     3584.3023
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## 1151                        91.77408                     1748.5465
## 1152                        91.77408                     1748.5465
## 1153                        96.92197                      217.1512
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## 1158                        96.92197                      217.1512
## 1159                        96.92197                      217.1512
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## 1168                        96.92197                      217.1512
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## 1176                        96.92197                      217.1512
## 1177                       224.17332                     5319.7674
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## 1200                       224.17332                     5319.7674
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## 1221                       149.58376                     3392.4419
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## 1224                       149.58376                     3392.4419
## 1225                       225.75980                     4970.9302
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## 1290                       118.66320                     2659.8837
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## 1293                       118.66320                     2659.8837
## 1294                       118.66320                     2659.8837
## 1295                       118.66320                     2659.8837
## 1296                       118.66320                     2659.8837
## 1297                       103.95027                     4765.9884
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## 1317                       103.95027                     4765.9884
## 1318                       103.95027                     4765.9884
## 1319                       103.95027                     4765.9884
## 1320                       103.95027                     4765.9884
## 1321                        69.53686                     2376.4535
## 1322                        69.53686                     2376.4535
## 1323                        69.53686                     2376.4535
## 1324                        69.53686                     2376.4535
## 1325                        69.53686                     2376.4535
## 1326                        69.53686                     2376.4535
## 1327                        69.53686                     2376.4535
## 1328                        69.53686                     2376.4535
## 1329                        69.53686                     2376.4535
## 1330                        69.53686                     2376.4535
## 1331                        69.53686                     2376.4535
## 1332                        69.53686                     2376.4535
## 1333                        69.53686                     2376.4535
## 1334                        69.53686                     2376.4535
## 1335                        69.53686                     2376.4535
## 1336                        69.53686                     2376.4535
## 1337                        69.53686                     2376.4535
## 1338                        69.53686                     2376.4535
## 1339                        69.53686                     2376.4535
## 1340                        69.53686                     2376.4535
## 1341                        69.53686                     2376.4535
## 1342                        69.53686                     2376.4535
## 1343                        69.53686                     2376.4535
## 1344                        69.53686                     2376.4535
## 1345                        96.92197                      217.1512
## 1346                        96.92197                      217.1512
## 1347                        96.92197                      217.1512
## 1348                        96.92197                      217.1512
## 1349                        96.92197                      217.1512
## 1350                        96.92197                      217.1512
## 1351                        96.92197                      217.1512
## 1352                        96.92197                      217.1512
## 1353                        96.92197                      217.1512
## 1354                        96.92197                      217.1512
## 1355                        96.92197                      217.1512
## 1356                        96.92197                      217.1512
## 1357                        96.92197                      217.1512
## 1358                        96.92197                      217.1512
## 1359                        96.92197                      217.1512
## 1360                        96.92197                      217.1512
## 1361                        96.92197                      217.1512
## 1362                        96.92197                      217.1512
## 1363                        96.92197                      217.1512
## 1364                        96.92197                      217.1512
## 1365                        96.92197                      217.1512
## 1366                        96.92197                      217.1512
## 1367                        96.92197                      217.1512
## 1368                        96.92197                      217.1512
## 1369                       178.88926                     3270.3488
## 1370                       178.88926                     3270.3488
## 1371                       178.88926                     3270.3488
## 1372                       178.88926                     3270.3488
## 1373                       178.88926                     3270.3488
## 1374                       178.88926                     3270.3488
## 1375                       178.88926                     3270.3488
## 1376                       178.88926                     3270.3488
## 1377                       178.88926                     3270.3488
## 1378                       178.88926                     3270.3488
## 1379                       178.88926                     3270.3488
## 1380                       178.88926                     3270.3488
## 1381                       178.88926                     3270.3488
## 1382                       178.88926                     3270.3488
## 1383                       178.88926                     3270.3488
## 1384                       178.88926                     3270.3488
## 1385                       178.88926                     3270.3488
## 1386                       178.88926                     3270.3488
## 1387                       178.88926                     3270.3488
## 1388                       178.88926                     3270.3488
## 1389                       178.88926                     3270.3488
## 1390                       178.88926                     3270.3488
## 1391                       178.88926                     3270.3488
## 1392                       178.88926                     3270.3488
## 1393                       199.80974                     4203.4884
## 1394                       199.80974                     4203.4884
## 1395                       199.80974                     4203.4884
## 1396                       199.80974                     4203.4884
## 1397                       199.80974                     4203.4884
## 1398                       199.80974                     4203.4884
## 1399                       199.80974                     4203.4884
## 1400                       199.80974                     4203.4884
## 1401                       199.80974                     4203.4884
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## 1403                       199.80974                     4203.4884
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## 1406                       199.80974                     4203.4884
## 1407                       199.80974                     4203.4884
## 1408                       199.80974                     4203.4884
## 1409                       199.80974                     4203.4884
## 1410                       199.80974                     4203.4884
## 1411                       199.80974                     4203.4884
## 1412                       199.80974                     4203.4884
## 1413                       199.80974                     4203.4884
## 1414                       199.80974                     4203.4884
## 1415                       199.80974                     4203.4884
## 1416                       199.80974                     4203.4884
## 1417                       157.48855                     4709.3023
## 1418                       157.48855                     4709.3023
## 1419                       157.48855                     4709.3023
## 1420                       157.48855                     4709.3023
## 1421                       157.48855                     4709.3023
## 1422                       157.48855                     4709.3023
## 1423                       157.48855                     4709.3023
## 1424                       157.48855                     4709.3023
## 1425                       157.48855                     4709.3023
## 1426                       157.48855                     4709.3023
## 1427                       157.48855                     4709.3023
## 1428                       157.48855                     4709.3023
## 1429                       157.48855                     4709.3023
## 1430                       157.48855                     4709.3023
## 1431                       157.48855                     4709.3023
## 1432                       157.48855                     4709.3023
## 1433                       157.48855                     4709.3023
## 1434                       157.48855                     4709.3023
## 1435                       157.48855                     4709.3023
## 1436                       157.48855                     4709.3023
## 1437                       157.48855                     4709.3023
## 1438                       157.48855                     4709.3023
## 1439                       157.48855                     4709.3023
## 1440                       157.48855                     4709.3023
## 1441                       146.54265                     5345.9302
## 1442                       146.54265                     5345.9302
## 1443                       146.54265                     5345.9302
## 1444                       146.54265                     5345.9302
## 1445                       146.54265                     5345.9302
## 1446                       146.54265                     5345.9302
## 1447                       146.54265                     5345.9302
## 1448                       146.54265                     5345.9302
## 1449                       146.54265                     5345.9302
## 1450                       146.54265                     5345.9302
## 1451                       146.54265                     5345.9302
## 1452                       146.54265                     5345.9302
## 1453                       146.54265                     5345.9302
## 1454                       146.54265                     5345.9302
## 1455                       146.54265                     5345.9302
## 1456                       146.54265                     5345.9302
## 1457                       146.54265                     5345.9302
## 1458                       146.54265                     5345.9302
## 1459                       146.54265                     5345.9302
## 1460                       146.54265                     5345.9302
## 1461                       146.54265                     5345.9302
## 1462                       146.54265                     5345.9302
## 1463                       146.54265                     5345.9302
## 1464                       146.54265                     5345.9302
## 1465                       139.36826                     7709.3023
## 1466                       139.36826                     7709.3023
## 1467                       139.36826                     7709.3023
## 1468                       139.36826                     7709.3023
## 1469                       139.36826                     7709.3023
## 1470                       139.36826                     7709.3023
## 1471                       139.36826                     7709.3023
## 1472                       139.36826                     7709.3023
## 1473                       139.36826                     7709.3023
## 1474                       139.36826                     7709.3023
## 1475                       139.36826                     7709.3023
## 1476                       139.36826                     7709.3023
## 1477                       139.36826                     7709.3023
## 1478                       139.36826                     7709.3023
## 1479                       139.36826                     7709.3023
## 1480                       139.36826                     7709.3023
## 1481                       139.36826                     7709.3023
## 1482                       139.36826                     7709.3023
## 1483                       139.36826                     7709.3023
## 1484                       139.36826                     7709.3023
## 1485                       139.36826                     7709.3023
## 1486                       139.36826                     7709.3023
## 1487                       139.36826                     7709.3023
## 1488                       139.36826                     7709.3023
## 1489                        98.96723                     3553.7791
## 1490                        98.96723                     3553.7791
## 1491                        98.96723                     3553.7791
## 1492                        98.96723                     3553.7791
## 1493                        98.96723                     3553.7791
## 1494                        98.96723                     3553.7791
## 1495                        98.96723                     3553.7791
## 1496                        98.96723                     3553.7791
## 1497                        98.96723                     3553.7791
## 1498                        98.96723                     3553.7791
## 1499                        98.96723                     3553.7791
## 1500                        98.96723                     3553.7791
## 1501                        98.96723                     3553.7791
## 1502                        98.96723                     3553.7791
## 1503                        98.96723                     3553.7791
## 1504                        98.96723                     3553.7791
## 1505                        98.96723                     3553.7791
## 1506                        98.96723                     3553.7791
## 1507                        98.96723                     3553.7791
## 1508                        98.96723                     3553.7791
## 1509                        98.96723                     3553.7791
## 1510                        98.96723                     3553.7791
## 1511                        98.96723                     3553.7791
## 1512                        98.96723                     3553.7791
## 1513                       140.28302                     2180.2326
## 1514                       140.28302                     2180.2326
## 1515                       140.28302                     2180.2326
## 1516                       140.28302                     2180.2326
## 1517                       140.28302                     2180.2326
## 1518                       140.28302                     2180.2326
## 1519                       140.28302                     2180.2326
## 1520                       140.28302                     2180.2326
## 1521                       140.28302                     2180.2326
## 1522                       140.28302                     2180.2326
## 1523                       140.28302                     2180.2326
## 1524                       140.28302                     2180.2326
## 1525                       140.28302                     2180.2326
## 1526                       140.28302                     2180.2326
## 1527                       140.28302                     2180.2326
## 1528                       140.28302                     2180.2326
## 1529                       140.28302                     2180.2326
## 1530                       140.28302                     2180.2326
## 1531                       140.28302                     2180.2326
## 1532                       140.28302                     2180.2326
## 1533                       140.28302                     2180.2326
## 1534                       140.28302                     2180.2326
## 1535                       140.28302                     2180.2326
## 1536                       140.28302                     2180.2326
## 1537                        96.92197                      217.1512
## 1538                        96.92197                      217.1512
## 1539                        96.92197                      217.1512
## 1540                        96.92197                      217.1512
## 1541                        96.92197                      217.1512
## 1542                        96.92197                      217.1512
## 1543                        96.92197                      217.1512
## 1544                        96.92197                      217.1512
## 1545                        96.92197                      217.1512
## 1546                        96.92197                      217.1512
## 1547                        96.92197                      217.1512
## 1548                        96.92197                      217.1512
## 1549                        96.92197                      217.1512
## 1550                        96.92197                      217.1512
## 1551                        96.92197                      217.1512
## 1552                        96.92197                      217.1512
## 1553                        96.92197                      217.1512
## 1554                        96.92197                      217.1512
## 1555                        96.92197                      217.1512
## 1556                        96.92197                      217.1512
## 1557                        96.92197                      217.1512
## 1558                        96.92197                      217.1512
## 1559                        96.92197                      217.1512
## 1560                        96.92197                      217.1512
## 1561                       110.30845                     1090.1163
## 1562                       110.30845                     1090.1163
## 1563                       110.30845                     1090.1163
## 1564                       110.30845                     1090.1163
## 1565                       110.30845                     1090.1163
## 1566                       110.30845                     1090.1163
## 1567                       110.30845                     1090.1163
## 1568                       110.30845                     1090.1163
## 1569                       110.30845                     1090.1163
## 1570                       110.30845                     1090.1163
## 1571                       110.30845                     1090.1163
## 1572                       110.30845                     1090.1163
## 1573                       110.30845                     1090.1163
## 1574                       110.30845                     1090.1163
## 1575                       110.30845                     1090.1163
## 1576                       110.30845                     1090.1163
## 1577                       110.30845                     1090.1163
## 1578                       110.30845                     1090.1163
## 1579                       110.30845                     1090.1163
## 1580                       110.30845                     1090.1163
## 1581                       110.30845                     1090.1163
## 1582                       110.30845                     1090.1163
## 1583                       110.30845                     1090.1163
## 1584                       110.30845                     1090.1163
## 1585                       177.73560                     1796.5116
## 1586                       177.73560                     1796.5116
## 1587                       177.73560                     1796.5116
## 1588                       177.73560                     1796.5116
## 1589                       177.73560                     1796.5116
## 1590                       177.73560                     1796.5116
## 1591                       177.73560                     1796.5116
## 1592                       177.73560                     1796.5116
## 1593                       177.73560                     1796.5116
## 1594                       177.73560                     1796.5116
## 1595                       177.73560                     1796.5116
## 1596                       177.73560                     1796.5116
## 1597                       177.73560                     1796.5116
## 1598                       177.73560                     1796.5116
## 1599                       177.73560                     1796.5116
## 1600                       177.73560                     1796.5116
## 1601                       177.73560                     1796.5116
## 1602                       177.73560                     1796.5116
## 1603                       177.73560                     1796.5116
## 1604                       177.73560                     1796.5116
## 1605                       177.73560                     1796.5116
## 1606                       177.73560                     1796.5116
## 1607                       177.73560                     1796.5116
## 1608                       177.73560                     1796.5116
## 1609                       114.02030                     2616.2791
## 1610                       114.02030                     2616.2791
## 1611                       114.02030                     2616.2791
## 1612                       114.02030                     2616.2791
## 1613                       114.02030                     2616.2791
## 1614                       114.02030                     2616.2791
## 1615                       114.02030                     2616.2791
## 1616                       114.02030                     2616.2791
## 1617                       114.02030                     2616.2791
## 1618                       114.02030                     2616.2791
## 1619                       114.02030                     2616.2791
## 1620                       114.02030                     2616.2791
## 1621                       114.02030                     2616.2791
## 1622                       114.02030                     2616.2791
## 1623                       114.02030                     2616.2791
## 1624                       114.02030                     2616.2791
## 1625                       114.02030                     2616.2791
## 1626                       114.02030                     2616.2791
## 1627                       114.02030                     2616.2791
## 1628                       114.02030                     2616.2791
## 1629                       114.02030                     2616.2791
## 1630                       114.02030                     2616.2791
## 1631                       114.02030                     2616.2791
## 1632                       114.02030                     2616.2791
## 1633                       146.27287                     3680.2326
## 1634                       146.27287                     3680.2326
## 1635                       146.27287                     3680.2326
## 1636                       146.27287                     3680.2326
## 1637                       146.27287                     3680.2326
## 1638                       146.27287                     3680.2326
## 1639                       146.27287                     3680.2326
## 1640                       146.27287                     3680.2326
## 1641                       146.27287                     3680.2326
## 1642                       146.27287                     3680.2326
## 1643                       146.27287                     3680.2326
## 1644                       146.27287                     3680.2326
## 1645                       146.27287                     3680.2326
## 1646                       146.27287                     3680.2326
## 1647                       146.27287                     3680.2326
## 1648                       146.27287                     3680.2326
## 1649                       146.27287                     3680.2326
## 1650                       146.27287                     3680.2326
## 1651                       146.27287                     3680.2326
## 1652                       146.27287                     3680.2326
## 1653                       146.27287                     3680.2326
## 1654                       146.27287                     3680.2326
## 1655                       146.27287                     3680.2326
## 1656                       146.27287                     3680.2326
## 1657                        59.54311                     4360.4651
## 1658                        59.54311                     4360.4651
## 1659                        59.54311                     4360.4651
## 1660                        59.54311                     4360.4651
## 1661                        59.54311                     4360.4651
## 1662                        59.54311                     4360.4651
## 1663                        59.54311                     4360.4651
## 1664                        59.54311                     4360.4651
## 1665                        59.54311                     4360.4651
## 1666                        59.54311                     4360.4651
## 1667                        59.54311                     4360.4651
## 1668                        59.54311                     4360.4651
## 1669                        59.54311                     4360.4651
## 1670                        59.54311                     4360.4651
## 1671                        59.54311                     4360.4651
## 1672                        59.54311                     4360.4651
## 1673                        59.54311                     4360.4651
## 1674                        59.54311                     4360.4651
## 1675                        59.54311                     4360.4651
## 1676                        59.54311                     4360.4651
## 1677                        59.54311                     4360.4651
## 1678                        59.54311                     4360.4651
## 1679                        59.54311                     4360.4651
## 1680                        59.54311                     4360.4651
## 1681                        39.67455                     1090.1163
## 1682                        39.67455                     1090.1163
## 1683                        39.67455                     1090.1163
## 1684                        39.67455                     1090.1163
## 1685                        39.67455                     1090.1163
## 1686                        39.67455                     1090.1163
## 1687                        39.67455                     1090.1163
## 1688                        39.67455                     1090.1163
## 1689                        39.67455                     1090.1163
## 1690                        39.67455                     1090.1163
## 1691                        39.67455                     1090.1163
## 1692                        39.67455                     1090.1163
## 1693                        39.67455                     1090.1163
## 1694                        39.67455                     1090.1163
## 1695                        39.67455                     1090.1163
## 1696                        39.67455                     1090.1163
## 1697                        39.67455                     1090.1163
## 1698                        39.67455                     1090.1163
## 1699                        39.67455                     1090.1163
## 1700                        39.67455                     1090.1163
## 1701                        39.67455                     1090.1163
## 1702                        39.67455                     1090.1163
## 1703                        39.67455                     1090.1163
## 1704                        39.67455                     1090.1163
## 1705                        31.70159                     2180.2326
## 1706                        31.70159                     2180.2326
## 1707                        31.70159                     2180.2326
## 1708                        31.70159                     2180.2326
## 1709                        31.70159                     2180.2326
## 1710                        31.70159                     2180.2326
## 1711                        31.70159                     2180.2326
## 1712                        31.70159                     2180.2326
## 1713                        31.70159                     2180.2326
## 1714                        31.70159                     2180.2326
## 1715                        31.70159                     2180.2326
## 1716                        31.70159                     2180.2326
## 1717                        31.70159                     2180.2326
## 1718                        31.70159                     2180.2326
## 1719                        31.70159                     2180.2326
## 1720                        31.70159                     2180.2326
## 1721                        31.70159                     2180.2326
## 1722                        31.70159                     2180.2326
## 1723                        31.70159                     2180.2326
## 1724                        31.70159                     2180.2326
## 1725                        31.70159                     2180.2326
## 1726                        31.70159                     2180.2326
## 1727                        31.70159                     2180.2326
## 1728                        31.70159                     2180.2326
## 1729                        96.92197                      217.1512
## 1730                        96.92197                      217.1512
## 1731                        96.92197                      217.1512
## 1732                        96.92197                      217.1512
## 1733                        96.92197                      217.1512
## 1734                        96.92197                      217.1512
## 1735                        96.92197                      217.1512
## 1736                        96.92197                      217.1512
## 1737                        96.92197                      217.1512
## 1738                        96.92197                      217.1512
## 1739                        96.92197                      217.1512
## 1740                        96.92197                      217.1512
## 1741                        96.92197                      217.1512
## 1742                        96.92197                      217.1512
## 1743                        96.92197                      217.1512
## 1744                        96.92197                      217.1512
## 1745                        96.92197                      217.1512
## 1746                        96.92197                      217.1512
## 1747                        96.92197                      217.1512
## 1748                        96.92197                      217.1512
## 1749                        96.92197                      217.1512
## 1750                        96.92197                      217.1512
## 1751                        96.92197                      217.1512
## 1752                        96.92197                      217.1512
## 1753                       115.04038                     1090.1163
## 1754                       115.04038                     1090.1163
## 1755                       115.04038                     1090.1163
## 1756                       115.04038                     1090.1163
## 1757                       115.04038                     1090.1163
## 1758                       115.04038                     1090.1163
## 1759                       115.04038                     1090.1163
## 1760                       115.04038                     1090.1163
## 1761                       115.04038                     1090.1163
## 1762                       115.04038                     1090.1163
## 1763                       115.04038                     1090.1163
## 1764                       115.04038                     1090.1163
## 1765                       115.04038                     1090.1163
## 1766                       115.04038                     1090.1163
## 1767                       115.04038                     1090.1163
## 1768                       115.04038                     1090.1163
## 1769                       115.04038                     1090.1163
## 1770                       115.04038                     1090.1163
## 1771                       115.04038                     1090.1163
## 1772                       115.04038                     1090.1163
## 1773                       115.04038                     1090.1163
## 1774                       115.04038                     1090.1163
## 1775                       115.04038                     1090.1163
## 1776                       115.04038                     1090.1163
## 1777                       170.95069                     2886.6279
## 1778                       170.95069                     2886.6279
## 1779                       170.95069                     2886.6279
## 1780                       170.95069                     2886.6279
## 1781                       170.95069                     2886.6279
## 1782                       170.95069                     2886.6279
## 1783                       170.95069                     2886.6279
## 1784                       170.95069                     2886.6279
## 1785                       170.95069                     2886.6279
## 1786                       170.95069                     2886.6279
## 1787                       170.95069                     2886.6279
## 1788                       170.95069                     2886.6279
## 1789                       170.95069                     2886.6279
## 1790                       170.95069                     2886.6279
## 1791                       170.95069                     2886.6279
## 1792                       170.95069                     2886.6279
## 1793                       170.95069                     2886.6279
## 1794                       170.95069                     2886.6279
## 1795                       170.95069                     2886.6279
## 1796                       170.95069                     2886.6279
## 1797                       170.95069                     2886.6279
## 1798                       170.95069                     2886.6279
## 1799                       170.95069                     2886.6279
## 1800                       170.95069                     2886.6279
## 1801                       207.04408                     2616.2791
## 1802                       207.04408                     2616.2791
## 1803                       207.04408                     2616.2791
## 1804                       207.04408                     2616.2791
## 1805                       207.04408                     2616.2791
## 1806                       207.04408                     2616.2791
## 1807                       207.04408                     2616.2791
## 1808                       207.04408                     2616.2791
## 1809                       207.04408                     2616.2791
## 1810                       207.04408                     2616.2791
## 1811                       207.04408                     2616.2791
## 1812                       207.04408                     2616.2791
## 1813                       207.04408                     2616.2791
## 1814                       207.04408                     2616.2791
## 1815                       207.04408                     2616.2791
## 1816                       207.04408                     2616.2791
## 1817                       207.04408                     2616.2791
## 1818                       207.04408                     2616.2791
## 1819                       207.04408                     2616.2791
## 1820                       207.04408                     2616.2791
## 1821                       207.04408                     2616.2791
## 1822                       207.04408                     2616.2791
## 1823                       207.04408                     2616.2791
## 1824                       207.04408                     2616.2791
## 1825                       204.76185                     1090.1163
## 1826                       204.76185                     1090.1163
## 1827                       204.76185                     1090.1163
## 1828                       204.76185                     1090.1163
## 1829                       204.76185                     1090.1163
## 1830                       204.76185                     1090.1163
## 1831                       204.76185                     1090.1163
## 1832                       204.76185                     1090.1163
## 1833                       204.76185                     1090.1163
## 1834                       204.76185                     1090.1163
## 1835                       204.76185                     1090.1163
## 1836                       204.76185                     1090.1163
## 1837                       204.76185                     1090.1163
## 1838                       204.76185                     1090.1163
## 1839                       204.76185                     1090.1163
## 1840                       204.76185                     1090.1163
## 1841                       204.76185                     1090.1163
## 1842                       204.76185                     1090.1163
## 1843                       204.76185                     1090.1163
## 1844                       204.76185                     1090.1163
## 1845                       204.76185                     1090.1163
## 1846                       204.76185                     1090.1163
## 1847                       204.76185                     1090.1163
## 1848                       204.76185                     1090.1163
## 1849                       170.47197                     2162.7907
## 1850                       170.47197                     2162.7907
## 1851                       170.47197                     2162.7907
## 1852                       170.47197                     2162.7907
## 1853                       170.47197                     2162.7907
## 1854                       170.47197                     2162.7907
## 1855                       170.47197                     2162.7907
## 1856                       170.47197                     2162.7907
## 1857                       170.47197                     2162.7907
## 1858                       170.47197                     2162.7907
## 1859                       170.47197                     2162.7907
## 1860                       170.47197                     2162.7907
## 1861                       170.47197                     2162.7907
## 1862                       170.47197                     2162.7907
## 1863                       170.47197                     2162.7907
## 1864                       170.47197                     2162.7907
## 1865                       170.47197                     2162.7907
## 1866                       170.47197                     2162.7907
## 1867                       170.47197                     2162.7907
## 1868                       170.47197                     2162.7907
## 1869                       170.47197                     2162.7907
## 1870                       170.47197                     2162.7907
## 1871                       170.47197                     2162.7907
## 1872                       170.47197                     2162.7907
## 1873                       149.52700                     4329.9419
## 1874                       149.52700                     4329.9419
## 1875                       149.52700                     4329.9419
## 1876                       149.52700                     4329.9419
## 1877                       149.52700                     4329.9419
## 1878                       149.52700                     4329.9419
## 1879                       149.52700                     4329.9419
## 1880                       149.52700                     4329.9419
## 1881                       149.52700                     4329.9419
## 1882                       149.52700                     4329.9419
## 1883                       149.52700                     4329.9419
## 1884                       149.52700                     4329.9419
## 1885                       149.52700                     4329.9419
## 1886                       149.52700                     4329.9419
## 1887                       149.52700                     4329.9419
## 1888                       149.52700                     4329.9419
## 1889                       149.52700                     4329.9419
## 1890                       149.52700                     4329.9419
## 1891                       149.52700                     4329.9419
## 1892                       149.52700                     4329.9419
## 1893                       149.52700                     4329.9419
## 1894                       149.52700                     4329.9419
## 1895                       149.52700                     4329.9419
## 1896                       149.52700                     4329.9419
## 1897                       110.26880                     3920.0581
## 1898                       110.26880                     3920.0581
## 1899                       110.26880                     3920.0581
## 1900                       110.26880                     3920.0581
## 1901                       110.26880                     3920.0581
## 1902                       110.26880                     3920.0581
## 1903                       110.26880                     3920.0581
## 1904                       110.26880                     3920.0581
## 1905                       110.26880                     3920.0581
## 1906                       110.26880                     3920.0581
## 1907                       110.26880                     3920.0581
## 1908                       110.26880                     3920.0581
## 1909                       110.26880                     3920.0581
## 1910                       110.26880                     3920.0581
## 1911                       110.26880                     3920.0581
## 1912                       110.26880                     3920.0581
## 1913                       110.26880                     3920.0581
## 1914                       110.26880                     3920.0581
## 1915                       110.26880                     3920.0581
## 1916                       110.26880                     3920.0581
## 1917                       110.26880                     3920.0581
## 1918                       110.26880                     3920.0581
## 1919                       110.26880                     3920.0581
## 1920                       110.26880                     3920.0581
## 1921                        96.92197                      217.1512
## 1922                        96.92197                      217.1512
## 1923                        96.92197                      217.1512
## 1924                        96.92197                      217.1512
## 1925                        96.92197                      217.1512
## 1926                        96.92197                      217.1512
## 1927                        96.92197                      217.1512
## 1928                        96.92197                      217.1512
## 1929                        96.92197                      217.1512
## 1930                        96.92197                      217.1512
## 1931                        96.92197                      217.1512
## 1932                        96.92197                      217.1512
## 1933                        96.92197                      217.1512
## 1934                        96.92197                      217.1512
## 1935                        96.92197                      217.1512
## 1936                        96.92197                      217.1512
## 1937                        96.92197                      217.1512
## 1938                        96.92197                      217.1512
## 1939                        96.92197                      217.1512
## 1940                        96.92197                      217.1512
## 1941                        96.92197                      217.1512
## 1942                        96.92197                      217.1512
## 1943                        96.92197                      217.1512
## 1944                        96.92197                      217.1512
## 1945                       151.39694                     1473.8372
## 1946                       151.39694                     1473.8372
## 1947                       151.39694                     1473.8372
## 1948                       151.39694                     1473.8372
## 1949                       151.39694                     1473.8372
## 1950                       151.39694                     1473.8372
## 1951                       151.39694                     1473.8372
## 1952                       151.39694                     1473.8372
## 1953                       151.39694                     1473.8372
## 1954                       151.39694                     1473.8372
## 1955                       151.39694                     1473.8372
## 1956                       151.39694                     1473.8372
## 1957                       151.39694                     1473.8372
## 1958                       151.39694                     1473.8372
## 1959                       151.39694                     1473.8372
## 1960                       151.39694                     1473.8372
## 1961                       151.39694                     1473.8372
## 1962                       151.39694                     1473.8372
## 1963                       151.39694                     1473.8372
## 1964                       151.39694                     1473.8372
## 1965                       151.39694                     1473.8372
## 1966                       151.39694                     1473.8372
## 1967                       151.39694                     1473.8372
## 1968                       151.39694                     1473.8372
## 1969                       158.17674                     2502.9070
## 1970                       158.17674                     2502.9070
## 1971                       158.17674                     2502.9070
## 1972                       158.17674                     2502.9070
## 1973                       158.17674                     2502.9070
## 1974                       158.17674                     2502.9070
## 1975                       158.17674                     2502.9070
## 1976                       158.17674                     2502.9070
## 1977                       158.17674                     2502.9070
## 1978                       158.17674                     2502.9070
## 1979                       158.17674                     2502.9070
## 1980                       158.17674                     2502.9070
## 1981                       158.17674                     2502.9070
## 1982                       158.17674                     2502.9070
## 1983                       158.17674                     2502.9070
## 1984                       158.17674                     2502.9070
## 1985                       158.17674                     2502.9070
## 1986                       158.17674                     2502.9070
## 1987                       158.17674                     2502.9070
## 1988                       158.17674                     2502.9070
## 1989                       158.17674                     2502.9070
## 1990                       158.17674                     2502.9070
## 1991                       158.17674                     2502.9070
## 1992                       158.17674                     2502.9070
## 1993                       196.58628                     3388.9535
## 1994                       196.58628                     3388.9535
## 1995                       196.58628                     3388.9535
## 1996                       196.58628                     3388.9535
## 1997                       196.58628                     3388.9535
## 1998                       196.58628                     3388.9535
## 1999                       196.58628                     3388.9535
## 2000                       196.58628                     3388.9535
## 2001                       196.58628                     3388.9535
## 2002                       196.58628                     3388.9535
## 2003                       196.58628                     3388.9535
## 2004                       196.58628                     3388.9535
## 2005                       196.58628                     3388.9535
## 2006                       196.58628                     3388.9535
## 2007                       196.58628                     3388.9535
## 2008                       196.58628                     3388.9535
## 2009                       196.58628                     3388.9535
## 2010                       196.58628                     3388.9535
## 2011                       196.58628                     3388.9535
## 2012                       196.58628                     3388.9535
## 2013                       196.58628                     3388.9535
## 2014                       196.58628                     3388.9535
## 2015                       196.58628                     3388.9535
## 2016                       196.58628                     3388.9535
## 2017                       136.02956                     3584.3023
## 2018                       136.02956                     3584.3023
## 2019                       136.02956                     3584.3023
## 2020                       136.02956                     3584.3023
## 2021                       136.02956                     3584.3023
## 2022                       136.02956                     3584.3023
## 2023                       136.02956                     3584.3023
## 2024                       136.02956                     3584.3023
## 2025                       136.02956                     3584.3023
## 2026                       136.02956                     3584.3023
## 2027                       136.02956                     3584.3023
## 2028                       136.02956                     3584.3023
## 2029                       136.02956                     3584.3023
## 2030                       136.02956                     3584.3023
## 2031                       136.02956                     3584.3023
## 2032                       136.02956                     3584.3023
## 2033                       136.02956                     3584.3023
## 2034                       136.02956                     3584.3023
## 2035                       136.02956                     3584.3023
## 2036                       136.02956                     3584.3023
## 2037                       136.02956                     3584.3023
## 2038                       136.02956                     3584.3023
## 2039                       136.02956                     3584.3023
## 2040                       136.02956                     3584.3023
## 2041                       116.45107                     3750.0000
## 2042                       116.45107                     3750.0000
## 2043                       116.45107                     3750.0000
## 2044                       116.45107                     3750.0000
## 2045                       116.45107                     3750.0000
## 2046                       116.45107                     3750.0000
## 2047                       116.45107                     3750.0000
## 2048                       116.45107                     3750.0000
## 2049                       116.45107                     3750.0000
## 2050                       116.45107                     3750.0000
## 2051                       116.45107                     3750.0000
## 2052                       116.45107                     3750.0000
## 2053                       116.45107                     3750.0000
## 2054                       116.45107                     3750.0000
## 2055                       116.45107                     3750.0000
## 2056                       116.45107                     3750.0000
## 2057                       116.45107                     3750.0000
## 2058                       116.45107                     3750.0000
## 2059                       116.45107                     3750.0000
## 2060                       116.45107                     3750.0000
## 2061                       116.45107                     3750.0000
## 2062                       116.45107                     3750.0000
## 2063                       116.45107                     3750.0000
## 2064                       116.45107                     3750.0000
## 2065                       102.05987                     2768.8953
## 2066                       102.05987                     2768.8953
## 2067                       102.05987                     2768.8953
## 2068                       102.05987                     2768.8953
## 2069                       102.05987                     2768.8953
## 2070                       102.05987                     2768.8953
## 2071                       102.05987                     2768.8953
## 2072                       102.05987                     2768.8953
## 2073                       102.05987                     2768.8953
## 2074                       102.05987                     2768.8953
## 2075                       102.05987                     2768.8953
## 2076                       102.05987                     2768.8953
## 2077                       102.05987                     2768.8953
## 2078                       102.05987                     2768.8953
## 2079                       102.05987                     2768.8953
## 2080                       102.05987                     2768.8953
## 2081                       102.05987                     2768.8953
## 2082                       102.05987                     2768.8953
## 2083                       102.05987                     2768.8953
## 2084                       102.05987                     2768.8953
## 2085                       102.05987                     2768.8953
## 2086                       102.05987                     2768.8953
## 2087                       102.05987                     2768.8953
## 2088                       102.05987                     2768.8953
## 2089                        93.36916                     1748.5465
## 2090                        93.36916                     1748.5465
## 2091                        93.36916                     1748.5465
## 2092                        93.36916                     1748.5465
## 2093                        93.36916                     1748.5465
## 2094                        93.36916                     1748.5465
## 2095                        93.36916                     1748.5465
## 2096                        93.36916                     1748.5465
## 2097                        93.36916                     1748.5465
## 2098                        93.36916                     1748.5465
## 2099                        93.36916                     1748.5465
## 2100                        93.36916                     1748.5465
## 2101                        93.36916                     1748.5465
## 2102                        93.36916                     1748.5465
## 2103                        93.36916                     1748.5465
## 2104                        93.36916                     1748.5465
## 2105                        93.36916                     1748.5465
## 2106                        93.36916                     1748.5465
## 2107                        93.36916                     1748.5465
## 2108                        93.36916                     1748.5465
## 2109                        93.36916                     1748.5465
## 2110                        93.36916                     1748.5465
## 2111                        93.36916                     1748.5465
## 2112                        93.36916                     1748.5465
## 2113                        96.92197                      217.1512
## 2114                        96.92197                      217.1512
## 2115                        96.92197                      217.1512
## 2116                        96.92197                      217.1512
## 2117                        96.92197                      217.1512
## 2118                        96.92197                      217.1512
## 2119                        96.92197                      217.1512
## 2120                        96.92197                      217.1512
## 2121                        96.92197                      217.1512
## 2122                        96.92197                      217.1512
## 2123                        96.92197                      217.1512
## 2124                        96.92197                      217.1512
## 2125                        96.92197                      217.1512
## 2126                        96.92197                      217.1512
## 2127                        96.92197                      217.1512
## 2128                        96.92197                      217.1512
## 2129                        96.92197                      217.1512
## 2130                        96.92197                      217.1512
## 2131                        96.92197                      217.1512
## 2132                        96.92197                      217.1512
## 2133                        96.92197                      217.1512
## 2134                        96.92197                      217.1512
## 2135                        96.92197                      217.1512
## 2136                        96.92197                      217.1512
## 2137                       228.26879                     5973.8372
## 2138                       228.26879                     5973.8372
## 2139                       228.26879                     5973.8372
## 2140                       228.26879                     5973.8372
## 2141                       228.26879                     5973.8372
## 2142                       228.26879                     5973.8372
## 2143                       228.26879                     5973.8372
## 2144                       228.26879                     5973.8372
## 2145                       228.26879                     5973.8372
## 2146                       228.26879                     5973.8372
## 2147                       228.26879                     5973.8372
## 2148                       228.26879                     5973.8372
## 2149                       228.26879                     5973.8372
## 2150                       228.26879                     5973.8372
## 2151                       228.26879                     5973.8372
## 2152                       228.26879                     5973.8372
## 2153                       228.26879                     5973.8372
## 2154                       228.26879                     5973.8372
## 2155                       228.26879                     5973.8372
## 2156                       228.26879                     5973.8372
## 2157                       228.26879                     5973.8372
## 2158                       228.26879                     5973.8372
## 2159                       228.26879                     5973.8372
## 2160                       228.26879                     5973.8372
## 2161                       128.33997                     3008.7209
## 2162                       128.33997                     3008.7209
## 2163                       128.33997                     3008.7209
## 2164                       128.33997                     3008.7209
## 2165                       128.33997                     3008.7209
## 2166                       128.33997                     3008.7209
## 2167                       128.33997                     3008.7209
## 2168                       128.33997                     3008.7209
## 2169                       128.33997                     3008.7209
## 2170                       128.33997                     3008.7209
## 2171                       128.33997                     3008.7209
## 2172                       128.33997                     3008.7209
## 2173                       128.33997                     3008.7209
## 2174                       128.33997                     3008.7209
## 2175                       128.33997                     3008.7209
## 2176                       128.33997                     3008.7209
## 2177                       128.33997                     3008.7209
## 2178                       128.33997                     3008.7209
## 2179                       128.33997                     3008.7209
## 2180                       128.33997                     3008.7209
## 2181                       128.33997                     3008.7209
## 2182                       128.33997                     3008.7209
## 2183                       128.33997                     3008.7209
## 2184                       128.33997                     3008.7209
## 2185                       223.90932                     4967.4419
## 2186                       223.90932                     4967.4419
## 2187                       223.90932                     4967.4419
## 2188                       223.90932                     4967.4419
## 2189                       223.90932                     4967.4419
## 2190                       223.90932                     4967.4419
## 2191                       223.90932                     4967.4419
## 2192                       223.90932                     4967.4419
## 2193                       223.90932                     4967.4419
## 2194                       223.90932                     4967.4419
## 2195                       223.90932                     4967.4419
## 2196                       223.90932                     4967.4419
## 2197                       223.90932                     4967.4419
## 2198                       223.90932                     4967.4419
## 2199                       223.90932                     4967.4419
## 2200                       223.90932                     4967.4419
## 2201                       223.90932                     4967.4419
## 2202                       223.90932                     4967.4419
## 2203                       223.90932                     4967.4419
## 2204                       223.90932                     4967.4419
## 2205                       223.90932                     4967.4419
## 2206                       223.90932                     4967.4419
## 2207                       223.90932                     4967.4419
## 2208                       223.90932                     4967.4419
## 2209                       172.69644                     3008.7209
## 2210                       172.69644                     3008.7209
## 2211                       172.69644                     3008.7209
## 2212                       172.69644                     3008.7209
## 2213                       172.69644                     3008.7209
## 2214                       172.69644                     3008.7209
## 2215                       172.69644                     3008.7209
## 2216                       172.69644                     3008.7209
## 2217                       172.69644                     3008.7209
## 2218                       172.69644                     3008.7209
## 2219                       172.69644                     3008.7209
## 2220                       172.69644                     3008.7209
## 2221                       172.69644                     3008.7209
## 2222                       172.69644                     3008.7209
## 2223                       172.69644                     3008.7209
## 2224                       172.69644                     3008.7209
## 2225                       172.69644                     3008.7209
## 2226                       172.69644                     3008.7209
## 2227                       172.69644                     3008.7209
## 2228                       172.69644                     3008.7209
## 2229                       172.69644                     3008.7209
## 2230                       172.69644                     3008.7209
## 2231                       172.69644                     3008.7209
## 2232                       172.69644                     3008.7209
## 2233                       118.59016                     2659.8837
## 2234                       118.59016                     2659.8837
## 2235                       118.59016                     2659.8837
## 2236                       118.59016                     2659.8837
## 2237                       118.59016                     2659.8837
## 2238                       118.59016                     2659.8837
## 2239                       118.59016                     2659.8837
## 2240                       118.59016                     2659.8837
## 2241                       118.59016                     2659.8837
## 2242                       118.59016                     2659.8837
## 2243                       118.59016                     2659.8837
## 2244                       118.59016                     2659.8837
## 2245                       118.59016                     2659.8837
## 2246                       118.59016                     2659.8837
## 2247                       118.59016                     2659.8837
## 2248                       118.59016                     2659.8837
## 2249                       118.59016                     2659.8837
## 2250                       118.59016                     2659.8837
## 2251                       118.59016                     2659.8837
## 2252                       118.59016                     2659.8837
## 2253                       118.59016                     2659.8837
## 2254                       118.59016                     2659.8837
## 2255                       118.59016                     2659.8837
## 2256                       118.59016                     2659.8837
## 2257                       103.83993                     4765.9884
## 2258                       103.83993                     4765.9884
## 2259                       103.83993                     4765.9884
## 2260                       103.83993                     4765.9884
## 2261                       103.83993                     4765.9884
## 2262                       103.83993                     4765.9884
## 2263                       103.83993                     4765.9884
## 2264                       103.83993                     4765.9884
## 2265                       103.83993                     4765.9884
## 2266                       103.83993                     4765.9884
## 2267                       103.83993                     4765.9884
## 2268                       103.83993                     4765.9884
## 2269                       103.83993                     4765.9884
## 2270                       103.83993                     4765.9884
## 2271                       103.83993                     4765.9884
## 2272                       103.83993                     4765.9884
## 2273                       103.83993                     4765.9884
## 2274                       103.83993                     4765.9884
## 2275                       103.83993                     4765.9884
## 2276                       103.83993                     4765.9884
## 2277                       103.83993                     4765.9884
## 2278                       103.83993                     4765.9884
## 2279                       103.83993                     4765.9884
## 2280                       103.83993                     4765.9884
## 2281                        71.13194                     2376.4535
## 2282                        71.13194                     2376.4535
## 2283                        71.13194                     2376.4535
## 2284                        71.13194                     2376.4535
## 2285                        71.13194                     2376.4535
## 2286                        71.13194                     2376.4535
## 2287                        71.13194                     2376.4535
## 2288                        71.13194                     2376.4535
## 2289                        71.13194                     2376.4535
## 2290                        71.13194                     2376.4535
## 2291                        71.13194                     2376.4535
## 2292                        71.13194                     2376.4535
## 2293                        71.13194                     2376.4535
## 2294                        71.13194                     2376.4535
## 2295                        71.13194                     2376.4535
## 2296                        71.13194                     2376.4535
## 2297                        71.13194                     2376.4535
## 2298                        71.13194                     2376.4535
## 2299                        71.13194                     2376.4535
## 2300                        71.13194                     2376.4535
## 2301                        71.13194                     2376.4535
## 2302                        71.13194                     2376.4535
## 2303                        71.13194                     2376.4535
## 2304                        71.13194                     2376.4535
## 2305                        96.92197                      217.1512
## 2306                        96.92197                      217.1512
## 2307                        96.92197                      217.1512
## 2308                        96.92197                      217.1512
## 2309                        96.92197                      217.1512
## 2310                        96.92197                      217.1512
## 2311                        96.92197                      217.1512
## 2312                        96.92197                      217.1512
## 2313                        96.92197                      217.1512
## 2314                        96.92197                      217.1512
## 2315                        96.92197                      217.1512
## 2316                        96.92197                      217.1512
## 2317                        96.92197                      217.1512
## 2318                        96.92197                      217.1512
## 2319                        96.92197                      217.1512
## 2320                        96.92197                      217.1512
## 2321                        96.92197                      217.1512
## 2322                        96.92197                      217.1512
## 2323                        96.92197                      217.1512
## 2324                        96.92197                      217.1512
## 2325                        96.92197                      217.1512
## 2326                        96.92197                      217.1512
## 2327                        96.92197                      217.1512
## 2328                        96.92197                      217.1512
## 2329                       182.98473                     3924.4186
## 2330                       182.98473                     3924.4186
## 2331                       182.98473                     3924.4186
## 2332                       182.98473                     3924.4186
## 2333                       182.98473                     3924.4186
## 2334                       182.98473                     3924.4186
## 2335                       182.98473                     3924.4186
## 2336                       182.98473                     3924.4186
## 2337                       182.98473                     3924.4186
## 2338                       182.98473                     3924.4186
## 2339                       182.98473                     3924.4186
## 2340                       182.98473                     3924.4186
## 2341                       182.98473                     3924.4186
## 2342                       182.98473                     3924.4186
## 2343                       182.98473                     3924.4186
## 2344                       182.98473                     3924.4186
## 2345                       182.98473                     3924.4186
## 2346                       182.98473                     3924.4186
## 2347                       182.98473                     3924.4186
## 2348                       182.98473                     3924.4186
## 2349                       182.98473                     3924.4186
## 2350                       182.98473                     3924.4186
## 2351                       182.98473                     3924.4186
## 2352                       182.98473                     3924.4186
## 2353                       178.56595                     3819.7674
## 2354                       178.56595                     3819.7674
## 2355                       178.56595                     3819.7674
## 2356                       178.56595                     3819.7674
## 2357                       178.56595                     3819.7674
## 2358                       178.56595                     3819.7674
## 2359                       178.56595                     3819.7674
## 2360                       178.56595                     3819.7674
## 2361                       178.56595                     3819.7674
## 2362                       178.56595                     3819.7674
## 2363                       178.56595                     3819.7674
## 2364                       178.56595                     3819.7674
## 2365                       178.56595                     3819.7674
## 2366                       178.56595                     3819.7674
## 2367                       178.56595                     3819.7674
## 2368                       178.56595                     3819.7674
## 2369                       178.56595                     3819.7674
## 2370                       178.56595                     3819.7674
## 2371                       178.56595                     3819.7674
## 2372                       178.56595                     3819.7674
## 2373                       178.56595                     3819.7674
## 2374                       178.56595                     3819.7674
## 2375                       178.56595                     3819.7674
## 2376                       178.56595                     3819.7674
## 2377                       155.63806                     4705.8140
## 2378                       155.63806                     4705.8140
## 2379                       155.63806                     4705.8140
## 2380                       155.63806                     4705.8140
## 2381                       155.63806                     4705.8140
## 2382                       155.63806                     4705.8140
## 2383                       155.63806                     4705.8140
## 2384                       155.63806                     4705.8140
## 2385                       155.63806                     4705.8140
## 2386                       155.63806                     4705.8140
## 2387                       155.63806                     4705.8140
## 2388                       155.63806                     4705.8140
## 2389                       155.63806                     4705.8140
## 2390                       155.63806                     4705.8140
## 2391                       155.63806                     4705.8140
## 2392                       155.63806                     4705.8140
## 2393                       155.63806                     4705.8140
## 2394                       155.63806                     4705.8140
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## 2449                        98.85688                     3553.7791
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## 2472                        98.85688                     3553.7791
## 2473                       141.87810                     2180.2326
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## 2494                       141.87810                     2180.2326
## 2495                       141.87810                     2180.2326
## 2496                       141.87810                     2180.2326
## 2497                        96.92197                      217.1512
## 2498                        96.92197                      217.1512
## 2499                        96.92197                      217.1512
## 2500                        96.92197                      217.1512
## 2501                        96.92197                      217.1512
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## 2517                        96.92197                      217.1512
## 2518                        96.92197                      217.1512
## 2519                        96.92197                      217.1512
## 2520                        96.92197                      217.1512
## 2521                       114.40392                     1744.1860
## 2522                       114.40392                     1744.1860
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## 2544                       114.40392                     1744.1860
## 2545                       156.49181                     1412.7907
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## 2688                        33.29667                     2180.2326
## 2689                        96.92197                      217.1512
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## 2711                        96.92197                      217.1512
## 2712                        96.92197                      217.1512
## 2713                       119.13585                     1744.1860
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## 2832                       170.39894                     2162.7907
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## 2856                       149.41666                     4329.9419
## 2857                       111.86388                     3920.0581
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## 2880                       111.86388                     3920.0581
## 2881                        96.92197                      217.1512
## 2882                        96.92197                      217.1512
## 2883                        96.92197                      217.1512
## 2884                        96.92197                      217.1512
## 2885                        96.92197                      217.1512
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## 2888                        96.92197                      217.1512
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## 2899                        96.92197                      217.1512
## 2900                        96.92197                      217.1512
## 2901                        96.92197                      217.1512
## 2902                        96.92197                      217.1512
## 2903                        96.92197                      217.1512
## 2904                        96.92197                      217.1512
## 2905                       149.85701                      819.7674
## 2906                       149.85701                      819.7674
## 2907                       149.85701                      819.7674
## 2908                       149.85701                      819.7674
## 2909                       149.85701                      819.7674
## 2910                       149.85701                      819.7674
## 2911                       149.85701                      819.7674
## 2912                       149.85701                      819.7674
## 2913                       149.85701                      819.7674
## 2914                       149.85701                      819.7674
## 2915                       149.85701                      819.7674
## 2916                       149.85701                      819.7674
## 2917                       149.85701                      819.7674
## 2918                       149.85701                      819.7674
## 2919                       149.85701                      819.7674
## 2920                       149.85701                      819.7674
## 2921                       149.85701                      819.7674
## 2922                       149.85701                      819.7674
## 2923                       149.85701                      819.7674
## 2924                       149.85701                      819.7674
## 2925                       149.85701                      819.7674
## 2926                       149.85701                      819.7674
## 2927                       149.85701                      819.7674
## 2928                       149.85701                      819.7674
## 2929                       158.73461                     2373.8372
## 2930                       158.73461                     2373.8372
## 2931                       158.73461                     2373.8372
## 2932                       158.73461                     2373.8372
## 2933                       158.73461                     2373.8372
## 2934                       158.73461                     2373.8372
## 2935                       158.73461                     2373.8372
## 2936                       158.73461                     2373.8372
## 2937                       158.73461                     2373.8372
## 2938                       158.73461                     2373.8372
## 2939                       158.73461                     2373.8372
## 2940                       158.73461                     2373.8372
## 2941                       158.73461                     2373.8372
## 2942                       158.73461                     2373.8372
## 2943                       158.73461                     2373.8372
## 2944                       158.73461                     2373.8372
## 2945                       158.73461                     2373.8372
## 2946                       158.73461                     2373.8372
## 2947                       158.73461                     2373.8372
## 2948                       158.73461                     2373.8372
## 2949                       158.73461                     2373.8372
## 2950                       158.73461                     2373.8372
## 2951                       158.73461                     2373.8372
## 2952                       158.73461                     2373.8372
## 2953                       185.82102                     2956.3953
## 2954                       185.82102                     2956.3953
## 2955                       185.82102                     2956.3953
## 2956                       185.82102                     2956.3953
## 2957                       185.82102                     2956.3953
## 2958                       185.82102                     2956.3953
## 2959                       185.82102                     2956.3953
## 2960                       185.82102                     2956.3953
## 2961                       185.82102                     2956.3953
## 2962                       185.82102                     2956.3953
## 2963                       185.82102                     2956.3953
## 2964                       185.82102                     2956.3953
## 2965                       185.82102                     2956.3953
## 2966                       185.82102                     2956.3953
## 2967                       185.82102                     2956.3953
## 2968                       185.82102                     2956.3953
## 2969                       185.82102                     2956.3953
## 2970                       185.82102                     2956.3953
## 2971                       185.82102                     2956.3953
## 2972                       185.82102                     2956.3953
## 2973                       185.82102                     2956.3953
## 2974                       185.82102                     2956.3953
## 2975                       185.82102                     2956.3953
## 2976                       185.82102                     2956.3953
## 2977                       134.71835                     3802.3256
## 2978                       134.71835                     3802.3256
## 2979                       134.71835                     3802.3256
## 2980                       134.71835                     3802.3256
## 2981                       134.71835                     3802.3256
## 2982                       134.71835                     3802.3256
## 2983                       134.71835                     3802.3256
## 2984                       134.71835                     3802.3256
## 2985                       134.71835                     3802.3256
## 2986                       134.71835                     3802.3256
## 2987                       134.71835                     3802.3256
## 2988                       134.71835                     3802.3256
## 2989                       134.71835                     3802.3256
## 2990                       134.71835                     3802.3256
## 2991                       134.71835                     3802.3256
## 2992                       134.71835                     3802.3256
## 2993                       134.71835                     3802.3256
## 2994                       134.71835                     3802.3256
## 2995                       134.71835                     3802.3256
## 2996                       134.71835                     3802.3256
## 2997                       134.71835                     3802.3256
## 2998                       134.71835                     3802.3256
## 2999                       134.71835                     3802.3256
## 3000                       134.71835                     3802.3256
## 3001                       117.16941                     3750.0000
## 3002                       117.16941                     3750.0000
## 3003                       117.16941                     3750.0000
## 3004                       117.16941                     3750.0000
## 3005                       117.16941                     3750.0000
## 3006                       117.16941                     3750.0000
## 3007                       117.16941                     3750.0000
## 3008                       117.16941                     3750.0000
## 3009                       117.16941                     3750.0000
## 3010                       117.16941                     3750.0000
## 3011                       117.16941                     3750.0000
## 3012                       117.16941                     3750.0000
## 3013                       117.16941                     3750.0000
## 3014                       117.16941                     3750.0000
## 3015                       117.16941                     3750.0000
## 3016                       117.16941                     3750.0000
## 3017                       117.16941                     3750.0000
## 3018                       117.16941                     3750.0000
## 3019                       117.16941                     3750.0000
## 3020                       117.16941                     3750.0000
## 3021                       117.16941                     3750.0000
## 3022                       117.16941                     3750.0000
## 3023                       117.16941                     3750.0000
## 3024                       117.16941                     3750.0000
## 3025                       102.14115                     2768.8953
## 3026                       102.14115                     2768.8953
## 3027                       102.14115                     2768.8953
## 3028                       102.14115                     2768.8953
## 3029                       102.14115                     2768.8953
## 3030                       102.14115                     2768.8953
## 3031                       102.14115                     2768.8953
## 3032                       102.14115                     2768.8953
## 3033                       102.14115                     2768.8953
## 3034                       102.14115                     2768.8953
## 3035                       102.14115                     2768.8953
## 3036                       102.14115                     2768.8953
## 3037                       102.14115                     2768.8953
## 3038                       102.14115                     2768.8953
## 3039                       102.14115                     2768.8953
## 3040                       102.14115                     2768.8953
## 3041                       102.14115                     2768.8953
## 3042                       102.14115                     2768.8953
## 3043                       102.14115                     2768.8953
## 3044                       102.14115                     2768.8953
## 3045                       102.14115                     2768.8953
## 3046                       102.14115                     2768.8953
## 3047                       102.14115                     2768.8953
## 3048                       102.14115                     2768.8953
## 3049                        93.17670                     1785.1744
## 3050                        93.17670                     1785.1744
## 3051                        93.17670                     1785.1744
## 3052                        93.17670                     1785.1744
## 3053                        93.17670                     1785.1744
## 3054                        93.17670                     1785.1744
## 3055                        93.17670                     1785.1744
## 3056                        93.17670                     1785.1744
## 3057                        93.17670                     1785.1744
## 3058                        93.17670                     1785.1744
## 3059                        93.17670                     1785.1744
## 3060                        93.17670                     1785.1744
## 3061                        93.17670                     1785.1744
## 3062                        93.17670                     1785.1744
## 3063                        93.17670                     1785.1744
## 3064                        93.17670                     1785.1744
## 3065                        93.17670                     1785.1744
## 3066                        93.17670                     1785.1744
## 3067                        93.17670                     1785.1744
## 3068                        93.17670                     1785.1744
## 3069                        93.17670                     1785.1744
## 3070                        93.17670                     1785.1744
## 3071                        93.17670                     1785.1744
## 3072                        93.17670                     1785.1744
## 3073                        96.92197                      217.1512
## 3074                        96.92197                      217.1512
## 3075                        96.92197                      217.1512
## 3076                        96.92197                      217.1512
## 3077                        96.92197                      217.1512
## 3078                        96.92197                      217.1512
## 3079                        96.92197                      217.1512
## 3080                        96.92197                      217.1512
## 3081                        96.92197                      217.1512
## 3082                        96.92197                      217.1512
## 3083                        96.92197                      217.1512
## 3084                        96.92197                      217.1512
## 3085                        96.92197                      217.1512
## 3086                        96.92197                      217.1512
## 3087                        96.92197                      217.1512
## 3088                        96.92197                      217.1512
## 3089                        96.92197                      217.1512
## 3090                        96.92197                      217.1512
## 3091                        96.92197                      217.1512
## 3092                        96.92197                      217.1512
## 3093                        96.92197                      217.1512
## 3094                        96.92197                      217.1512
## 3095                        96.92197                      217.1512
## 3096                        96.92197                      217.1512
## 3097                       226.72885                     5319.7674
## 3098                       226.72885                     5319.7674
## 3099                       226.72885                     5319.7674
## 3100                       226.72885                     5319.7674
## 3101                       226.72885                     5319.7674
## 3102                       226.72885                     5319.7674
## 3103                       226.72885                     5319.7674
## 3104                       226.72885                     5319.7674
## 3105                       226.72885                     5319.7674
## 3106                       226.72885                     5319.7674
## 3107                       226.72885                     5319.7674
## 3108                       226.72885                     5319.7674
## 3109                       226.72885                     5319.7674
## 3110                       226.72885                     5319.7674
## 3111                       226.72885                     5319.7674
## 3112                       226.72885                     5319.7674
## 3113                       226.72885                     5319.7674
## 3114                       226.72885                     5319.7674
## 3115                       226.72885                     5319.7674
## 3116                       226.72885                     5319.7674
## 3117                       226.72885                     5319.7674
## 3118                       226.72885                     5319.7674
## 3119                       226.72885                     5319.7674
## 3120                       226.72885                     5319.7674
## 3121                       128.89784                     2879.6512
## 3122                       128.89784                     2879.6512
## 3123                       128.89784                     2879.6512
## 3124                       128.89784                     2879.6512
## 3125                       128.89784                     2879.6512
## 3126                       128.89784                     2879.6512
## 3127                       128.89784                     2879.6512
## 3128                       128.89784                     2879.6512
## 3129                       128.89784                     2879.6512
## 3130                       128.89784                     2879.6512
## 3131                       128.89784                     2879.6512
## 3132                       128.89784                     2879.6512
## 3133                       128.89784                     2879.6512
## 3134                       128.89784                     2879.6512
## 3135                       128.89784                     2879.6512
## 3136                       128.89784                     2879.6512
## 3137                       128.89784                     2879.6512
## 3138                       128.89784                     2879.6512
## 3139                       128.89784                     2879.6512
## 3140                       128.89784                     2879.6512
## 3141                       128.89784                     2879.6512
## 3142                       128.89784                     2879.6512
## 3143                       128.89784                     2879.6512
## 3144                       128.89784                     2879.6512
## 3145                       213.14406                     4534.8837
## 3146                       213.14406                     4534.8837
## 3147                       213.14406                     4534.8837
## 3148                       213.14406                     4534.8837
## 3149                       213.14406                     4534.8837
## 3150                       213.14406                     4534.8837
## 3151                       213.14406                     4534.8837
## 3152                       213.14406                     4534.8837
## 3153                       213.14406                     4534.8837
## 3154                       213.14406                     4534.8837
## 3155                       213.14406                     4534.8837
## 3156                       213.14406                     4534.8837
## 3157                       213.14406                     4534.8837
## 3158                       213.14406                     4534.8837
## 3159                       213.14406                     4534.8837
## 3160                       213.14406                     4534.8837
## 3161                       213.14406                     4534.8837
## 3162                       213.14406                     4534.8837
## 3163                       213.14406                     4534.8837
## 3164                       213.14406                     4534.8837
## 3165                       213.14406                     4534.8837
## 3166                       213.14406                     4534.8837
## 3167                       213.14406                     4534.8837
## 3168                       213.14406                     4534.8837
## 3169                       171.38523                     3226.7442
## 3170                       171.38523                     3226.7442
## 3171                       171.38523                     3226.7442
## 3172                       171.38523                     3226.7442
## 3173                       171.38523                     3226.7442
## 3174                       171.38523                     3226.7442
## 3175                       171.38523                     3226.7442
## 3176                       171.38523                     3226.7442
## 3177                       171.38523                     3226.7442
## 3178                       171.38523                     3226.7442
## 3179                       171.38523                     3226.7442
## 3180                       171.38523                     3226.7442
## 3181                       171.38523                     3226.7442
## 3182                       171.38523                     3226.7442
## 3183                       171.38523                     3226.7442
## 3184                       171.38523                     3226.7442
## 3185                       171.38523                     3226.7442
## 3186                       171.38523                     3226.7442
## 3187                       171.38523                     3226.7442
## 3188                       171.38523                     3226.7442
## 3189                       171.38523                     3226.7442
## 3190                       171.38523                     3226.7442
## 3191                       171.38523                     3226.7442
## 3192                       171.38523                     3226.7442
## 3193                       119.30850                     2659.8837
## 3194                       119.30850                     2659.8837
## 3195                       119.30850                     2659.8837
## 3196                       119.30850                     2659.8837
## 3197                       119.30850                     2659.8837
## 3198                       119.30850                     2659.8837
## 3199                       119.30850                     2659.8837
## 3200                       119.30850                     2659.8837
## 3201                       119.30850                     2659.8837
## 3202                       119.30850                     2659.8837
## 3203                       119.30850                     2659.8837
## 3204                       119.30850                     2659.8837
## 3205                       119.30850                     2659.8837
## 3206                       119.30850                     2659.8837
## 3207                       119.30850                     2659.8837
## 3208                       119.30850                     2659.8837
## 3209                       119.30850                     2659.8837
## 3210                       119.30850                     2659.8837
## 3211                       119.30850                     2659.8837
## 3212                       119.30850                     2659.8837
## 3213                       119.30850                     2659.8837
## 3214                       119.30850                     2659.8837
## 3215                       119.30850                     2659.8837
## 3216                       119.30850                     2659.8837
## 3217                       103.92120                     4765.9884
## 3218                       103.92120                     4765.9884
## 3219                       103.92120                     4765.9884
## 3220                       103.92120                     4765.9884
## 3221                       103.92120                     4765.9884
## 3222                       103.92120                     4765.9884
## 3223                       103.92120                     4765.9884
## 3224                       103.92120                     4765.9884
## 3225                       103.92120                     4765.9884
##      total_metaecosystem_Cep_indiv total_metaecosystem_Col_indiv
## 1                         1794.767                    4812.20930
## 2                         1794.767                    4812.20930
## 3                         1794.767                    4812.20930
## 4                         1794.767                    4812.20930
## 5                         1794.767                    4812.20930
## 6                         1794.767                    4812.20930
## 7                         1794.767                    4812.20930
## 8                         1794.767                    4812.20930
## 9                         1794.767                    4812.20930
## 10                        1794.767                    4812.20930
## 11                        1794.767                    4812.20930
## 12                        1794.767                    4812.20930
## 13                        1794.767                    4812.20930
## 14                        1794.767                    4812.20930
## 15                        1794.767                    4812.20930
## 16                        1794.767                    4812.20930
## 17                        1794.767                    4812.20930
## 18                        1794.767                    4812.20930
## 19                        1794.767                    4812.20930
## 20                        1794.767                    4812.20930
## 21                        1794.767                    4812.20930
## 22                        1794.767                    4812.20930
## 23                        1794.767                    4812.20930
## 24                        1794.767                    4812.20930
## 25                        8056.395                   20305.81395
## 26                        8056.395                   20305.81395
## 27                        8056.395                   20305.81395
## 28                        8056.395                   20305.81395
## 29                        8056.395                   20305.81395
## 30                        8056.395                   20305.81395
## 31                        8056.395                   20305.81395
## 32                        8056.395                   20305.81395
## 33                        8056.395                   20305.81395
## 34                        8056.395                   20305.81395
## 35                        8056.395                   20305.81395
## 36                        8056.395                   20305.81395
## 37                        8056.395                   20305.81395
## 38                        8056.395                   20305.81395
## 39                        8056.395                   20305.81395
## 40                        8056.395                   20305.81395
## 41                        8056.395                   20305.81395
## 42                        8056.395                   20305.81395
## 43                        8056.395                   20305.81395
## 44                        8056.395                   20305.81395
## 45                        8056.395                   20305.81395
## 46                        8056.395                   20305.81395
## 47                        8056.395                   20305.81395
## 48                        8056.395                   20305.81395
## 49                       11759.302                   16356.97674
## 50                       11759.302                   16356.97674
## 51                       11759.302                   16356.97674
## 52                       11759.302                   16356.97674
## 53                       11759.302                   16356.97674
## 54                       11759.302                   16356.97674
## 55                       11759.302                   16356.97674
## 56                       11759.302                   16356.97674
## 57                       11759.302                   16356.97674
## 58                       11759.302                   16356.97674
## 59                       11759.302                   16356.97674
## 60                       11759.302                   16356.97674
## 61                       11759.302                   16356.97674
## 62                       11759.302                   16356.97674
## 63                       11759.302                   16356.97674
## 64                       11759.302                   16356.97674
## 65                       11759.302                   16356.97674
## 66                       11759.302                   16356.97674
## 67                       11759.302                   16356.97674
## 68                       11759.302                   16356.97674
## 69                       11759.302                   16356.97674
## 70                       11759.302                   16356.97674
## 71                       11759.302                   16356.97674
## 72                       11759.302                   16356.97674
## 73                        6552.907                    6973.25581
## 74                        6552.907                    6973.25581
## 75                        6552.907                    6973.25581
## 76                        6552.907                    6973.25581
## 77                        6552.907                    6973.25581
## 78                        6552.907                    6973.25581
## 79                        6552.907                    6973.25581
## 80                        6552.907                    6973.25581
## 81                        6552.907                    6973.25581
## 82                        6552.907                    6973.25581
## 83                        6552.907                    6973.25581
## 84                        6552.907                    6973.25581
## 85                        6552.907                    6973.25581
## 86                        6552.907                    6973.25581
## 87                        6552.907                    6973.25581
## 88                        6552.907                    6973.25581
## 89                        6552.907                    6973.25581
## 90                        6552.907                    6973.25581
## 91                        6552.907                    6973.25581
## 92                        6552.907                    6973.25581
## 93                        6552.907                    6973.25581
## 94                        6552.907                    6973.25581
## 95                        6552.907                    6973.25581
## 96                        6552.907                    6973.25581
## 97                        9326.163                    3912.20930
## 98                        9326.163                    3912.20930
## 99                        9326.163                    3912.20930
## 100                       9326.163                    3912.20930
## 101                       9326.163                    3912.20930
## 102                       9326.163                    3912.20930
## 103                       9326.163                    3912.20930
## 104                       9326.163                    3912.20930
## 105                       9326.163                    3912.20930
## 106                       9326.163                    3912.20930
## 107                       9326.163                    3912.20930
## 108                       9326.163                    3912.20930
## 109                       9326.163                    3912.20930
## 110                       9326.163                    3912.20930
## 111                       9326.163                    3912.20930
## 112                       9326.163                    3912.20930
## 113                       9326.163                    3912.20930
## 114                       9326.163                    3912.20930
## 115                       9326.163                    3912.20930
## 116                       9326.163                    3912.20930
## 117                       9326.163                    3912.20930
## 118                       9326.163                    3912.20930
## 119                       9326.163                    3912.20930
## 120                       9326.163                    3912.20930
## 121                       9818.023                       0.00000
## 122                       9818.023                       0.00000
## 123                       9818.023                       0.00000
## 124                       9818.023                       0.00000
## 125                       9818.023                       0.00000
## 126                       9818.023                       0.00000
## 127                       9818.023                       0.00000
## 128                       9818.023                       0.00000
## 129                       9818.023                       0.00000
## 130                       9818.023                       0.00000
## 131                       9818.023                       0.00000
## 132                       9818.023                       0.00000
## 133                       9818.023                       0.00000
## 134                       9818.023                       0.00000
## 135                       9818.023                       0.00000
## 136                       9818.023                       0.00000
## 137                       9818.023                       0.00000
## 138                       9818.023                       0.00000
## 139                       9818.023                       0.00000
## 140                       9818.023                       0.00000
## 141                       9818.023                       0.00000
## 142                       9818.023                       0.00000
## 143                       9818.023                       0.00000
## 144                       9818.023                       0.00000
## 145                      15800.581                     418.60465
## 146                      15800.581                     418.60465
## 147                      15800.581                     418.60465
## 148                      15800.581                     418.60465
## 149                      15800.581                     418.60465
## 150                      15800.581                     418.60465
## 151                      15800.581                     418.60465
## 152                      15800.581                     418.60465
## 153                      15800.581                     418.60465
## 154                      15800.581                     418.60465
## 155                      15800.581                     418.60465
## 156                      15800.581                     418.60465
## 157                      15800.581                     418.60465
## 158                      15800.581                     418.60465
## 159                      15800.581                     418.60465
## 160                      15800.581                     418.60465
## 161                      15800.581                     418.60465
## 162                      15800.581                     418.60465
## 163                      15800.581                     418.60465
## 164                      15800.581                     418.60465
## 165                      15800.581                     418.60465
## 166                      15800.581                     418.60465
## 167                      15800.581                     418.60465
## 168                      15800.581                     418.60465
## 169                      15422.093                       0.00000
## 170                      15422.093                       0.00000
## 171                      15422.093                       0.00000
## 172                      15422.093                       0.00000
## 173                      15422.093                       0.00000
## 174                      15422.093                       0.00000
## 175                      15422.093                       0.00000
## 176                      15422.093                       0.00000
## 177                      15422.093                       0.00000
## 178                      15422.093                       0.00000
## 179                      15422.093                       0.00000
## 180                      15422.093                       0.00000
## 181                      15422.093                       0.00000
## 182                      15422.093                       0.00000
## 183                      15422.093                       0.00000
## 184                      15422.093                       0.00000
## 185                      15422.093                       0.00000
## 186                      15422.093                       0.00000
## 187                      15422.093                       0.00000
## 188                      15422.093                       0.00000
## 189                      15422.093                       0.00000
## 190                      15422.093                       0.00000
## 191                      15422.093                       0.00000
## 192                      15422.093                       0.00000
## 193                       1794.767                    4812.20930
## 194                       1794.767                    4812.20930
## 195                       1794.767                    4812.20930
## 196                       1794.767                    4812.20930
## 197                       1794.767                    4812.20930
## 198                       1794.767                    4812.20930
## 199                       1794.767                    4812.20930
## 200                       1794.767                    4812.20930
## 201                       1794.767                    4812.20930
## 202                       1794.767                    4812.20930
## 203                       1794.767                    4812.20930
## 204                       1794.767                    4812.20930
## 205                       1794.767                    4812.20930
## 206                       1794.767                    4812.20930
## 207                       1794.767                    4812.20930
## 208                       1794.767                    4812.20930
## 209                       1794.767                    4812.20930
## 210                       1794.767                    4812.20930
## 211                       1794.767                    4812.20930
## 212                       1794.767                    4812.20930
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## 1940                      1794.767                    4812.20930
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## 1952                      7573.256                   19557.55814
## 1953                      7573.256                   19557.55814
## 1954                      7573.256                   19557.55814
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## 1956                      7573.256                   19557.55814
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## 1959                      7573.256                   19557.55814
## 1960                      7573.256                   19557.55814
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## 1962                      7573.256                   19557.55814
## 1963                      7573.256                   19557.55814
## 1964                      7573.256                   19557.55814
## 1965                      7573.256                   19557.55814
## 1966                      7573.256                   19557.55814
## 1967                      7573.256                   19557.55814
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## 1970                     11834.302                   16130.23256
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## 1973                     11834.302                   16130.23256
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## 1978                     11834.302                   16130.23256
## 1979                     11834.302                   16130.23256
## 1980                     11834.302                   16130.23256
## 1981                     11834.302                   16130.23256
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## 1983                     11834.302                   16130.23256
## 1984                     11834.302                   16130.23256
## 1985                     11834.302                   16130.23256
## 1986                     11834.302                   16130.23256
## 1987                     11834.302                   16130.23256
## 1988                     11834.302                   16130.23256
## 1989                     11834.302                   16130.23256
## 1990                     11834.302                   16130.23256
## 1991                     11834.302                   16130.23256
## 1992                     11834.302                   16130.23256
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## 1994                      8497.674                    6549.41860
## 1995                      8497.674                    6549.41860
## 1996                      8497.674                    6549.41860
## 1997                      8497.674                    6549.41860
## 1998                      8497.674                    6549.41860
## 1999                      8497.674                    6549.41860
## 2000                      8497.674                    6549.41860
## 2001                      8497.674                    6549.41860
## 2002                      8497.674                    6549.41860
## 2003                      8497.674                    6549.41860
## 2004                      8497.674                    6549.41860
## 2005                      8497.674                    6549.41860
## 2006                      8497.674                    6549.41860
## 2007                      8497.674                    6549.41860
## 2008                      8497.674                    6549.41860
## 2009                      8497.674                    6549.41860
## 2010                      8497.674                    6549.41860
## 2011                      8497.674                    6549.41860
## 2012                      8497.674                    6549.41860
## 2013                      8497.674                    6549.41860
## 2014                      8497.674                    6549.41860
## 2015                      8497.674                    6549.41860
## 2016                      8497.674                    6549.41860
## 2017                      9950.581                    3514.53488
## 2018                      9950.581                    3514.53488
## 2019                      9950.581                    3514.53488
## 2020                      9950.581                    3514.53488
## 2021                      9950.581                    3514.53488
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## 2029                      9950.581                    3514.53488
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## 2897                      1794.767                    4812.20930
## 2898                      1794.767                    4812.20930
## 2899                      1794.767                    4812.20930
## 2900                      1794.767                    4812.20930
## 2901                      1794.767                    4812.20930
## 2902                      1794.767                    4812.20930
## 2903                      1794.767                    4812.20930
## 2904                      1794.767                    4812.20930
## 2905                      7111.047                   21390.69767
## 2906                      7111.047                   21390.69767
## 2907                      7111.047                   21390.69767
## 2908                      7111.047                   21390.69767
## 2909                      7111.047                   21390.69767
## 2910                      7111.047                   21390.69767
## 2911                      7111.047                   21390.69767
## 2912                      7111.047                   21390.69767
## 2913                      7111.047                   21390.69767
## 2914                      7111.047                   21390.69767
## 2915                      7111.047                   21390.69767
## 2916                      7111.047                   21390.69767
## 2917                      7111.047                   21390.69767
## 2918                      7111.047                   21390.69767
## 2919                      7111.047                   21390.69767
## 2920                      7111.047                   21390.69767
## 2921                      7111.047                   21390.69767
## 2922                      7111.047                   21390.69767
## 2923                      7111.047                   21390.69767
## 2924                      7111.047                   21390.69767
## 2925                      7111.047                   21390.69767
## 2926                      7111.047                   21390.69767
## 2927                      7111.047                   21390.69767
## 2928                      7111.047                   21390.69767
## 2929                     11391.279                   16950.00000
## 2930                     11391.279                   16950.00000
## 2931                     11391.279                   16950.00000
## 2932                     11391.279                   16950.00000
## 2933                     11391.279                   16950.00000
## 2934                     11391.279                   16950.00000
## 2935                     11391.279                   16950.00000
## 2936                     11391.279                   16950.00000
## 2937                     11391.279                   16950.00000
## 2938                     11391.279                   16950.00000
## 2939                     11391.279                   16950.00000
## 2940                     11391.279                   16950.00000
## 2941                     11391.279                   16950.00000
## 2942                     11391.279                   16950.00000
## 2943                     11391.279                   16950.00000
## 2944                     11391.279                   16950.00000
## 2945                     11391.279                   16950.00000
## 2946                     11391.279                   16950.00000
## 2947                     11391.279                   16950.00000
## 2948                     11391.279                   16950.00000
## 2949                     11391.279                   16950.00000
## 2950                     11391.279                   16950.00000
## 2951                     11391.279                   16950.00000
## 2952                     11391.279                   16950.00000
## 2953                      7470.349                    7334.30233
## 2954                      7470.349                    7334.30233
## 2955                      7470.349                    7334.30233
## 2956                      7470.349                    7334.30233
## 2957                      7470.349                    7334.30233
## 2958                      7470.349                    7334.30233
## 2959                      7470.349                    7334.30233
## 2960                      7470.349                    7334.30233
## 2961                      7470.349                    7334.30233
## 2962                      7470.349                    7334.30233
## 2963                      7470.349                    7334.30233
## 2964                      7470.349                    7334.30233
## 2965                      7470.349                    7334.30233
## 2966                      7470.349                    7334.30233
## 2967                      7470.349                    7334.30233
## 2968                      7470.349                    7334.30233
## 2969                      7470.349                    7334.30233
## 2970                      7470.349                    7334.30233
## 2971                      7470.349                    7334.30233
## 2972                      7470.349                    7334.30233
## 2973                      7470.349                    7334.30233
## 2974                      7470.349                    7334.30233
## 2975                      7470.349                    7334.30233
## 2976                      7470.349                    7334.30233
## 2977                      9181.395                    4083.13953
## 2978                      9181.395                    4083.13953
## 2979                      9181.395                    4083.13953
## 2980                      9181.395                    4083.13953
## 2981                      9181.395                    4083.13953
## 2982                      9181.395                    4083.13953
## 2983                      9181.395                    4083.13953
## 2984                      9181.395                    4083.13953
## 2985                      9181.395                    4083.13953
## 2986                      9181.395                    4083.13953
## 2987                      9181.395                    4083.13953
## 2988                      9181.395                    4083.13953
## 2989                      9181.395                    4083.13953
## 2990                      9181.395                    4083.13953
## 2991                      9181.395                    4083.13953
## 2992                      9181.395                    4083.13953
## 2993                      9181.395                    4083.13953
## 2994                      9181.395                    4083.13953
## 2995                      9181.395                    4083.13953
## 2996                      9181.395                    4083.13953
## 2997                      9181.395                    4083.13953
## 2998                      9181.395                    4083.13953
## 2999                      9181.395                    4083.13953
## 3000                      9181.395                    4083.13953
## 3001                      9556.395                       0.00000
## 3002                      9556.395                       0.00000
## 3003                      9556.395                       0.00000
## 3004                      9556.395                       0.00000
## 3005                      9556.395                       0.00000
## 3006                      9556.395                       0.00000
## 3007                      9556.395                       0.00000
## 3008                      9556.395                       0.00000
## 3009                      9556.395                       0.00000
## 3010                      9556.395                       0.00000
## 3011                      9556.395                       0.00000
## 3012                      9556.395                       0.00000
## 3013                      9556.395                       0.00000
## 3014                      9556.395                       0.00000
## 3015                      9556.395                       0.00000
## 3016                      9556.395                       0.00000
## 3017                      9556.395                       0.00000
## 3018                      9556.395                       0.00000
## 3019                      9556.395                       0.00000
## 3020                      9556.395                       0.00000
## 3021                      9556.395                       0.00000
## 3022                      9556.395                       0.00000
## 3023                      9556.395                       0.00000
## 3024                      9556.395                       0.00000
## 3025                     15595.640                     418.60465
## 3026                     15595.640                     418.60465
## 3027                     15595.640                     418.60465
## 3028                     15595.640                     418.60465
## 3029                     15595.640                     418.60465
## 3030                     15595.640                     418.60465
## 3031                     15595.640                     418.60465
## 3032                     15595.640                     418.60465
## 3033                     15595.640                     418.60465
## 3034                     15595.640                     418.60465
## 3035                     15595.640                     418.60465
## 3036                     15595.640                     418.60465
## 3037                     15595.640                     418.60465
## 3038                     15595.640                     418.60465
## 3039                     15595.640                     418.60465
## 3040                     15595.640                     418.60465
## 3041                     15595.640                     418.60465
## 3042                     15595.640                     418.60465
## 3043                     15595.640                     418.60465
## 3044                     15595.640                     418.60465
## 3045                     15595.640                     418.60465
## 3046                     15595.640                     418.60465
## 3047                     15595.640                     418.60465
## 3048                     15595.640                     418.60465
## 3049                     15213.663                       0.00000
## 3050                     15213.663                       0.00000
## 3051                     15213.663                       0.00000
## 3052                     15213.663                       0.00000
## 3053                     15213.663                       0.00000
## 3054                     15213.663                       0.00000
## 3055                     15213.663                       0.00000
## 3056                     15213.663                       0.00000
## 3057                     15213.663                       0.00000
## 3058                     15213.663                       0.00000
## 3059                     15213.663                       0.00000
## 3060                     15213.663                       0.00000
## 3061                     15213.663                       0.00000
## 3062                     15213.663                       0.00000
## 3063                     15213.663                       0.00000
## 3064                     15213.663                       0.00000
## 3065                     15213.663                       0.00000
## 3066                     15213.663                       0.00000
## 3067                     15213.663                       0.00000
## 3068                     15213.663                       0.00000
## 3069                     15213.663                       0.00000
## 3070                     15213.663                       0.00000
## 3071                     15213.663                       0.00000
## 3072                     15213.663                       0.00000
## 3073                      1794.767                    4812.20930
## 3074                      1794.767                    4812.20930
## 3075                      1794.767                    4812.20930
## 3076                      1794.767                    4812.20930
## 3077                      1794.767                    4812.20930
## 3078                      1794.767                    4812.20930
## 3079                      1794.767                    4812.20930
## 3080                      1794.767                    4812.20930
## 3081                      1794.767                    4812.20930
## 3082                      1794.767                    4812.20930
## 3083                      1794.767                    4812.20930
## 3084                      1794.767                    4812.20930
## 3085                      1794.767                    4812.20930
## 3086                      1794.767                    4812.20930
## 3087                      1794.767                    4812.20930
## 3088                      1794.767                    4812.20930
## 3089                      1794.767                    4812.20930
## 3090                      1794.767                    4812.20930
## 3091                      1794.767                    4812.20930
## 3092                      1794.767                    4812.20930
## 3093                      1794.767                    4812.20930
## 3094                      1794.767                    4812.20930
## 3095                      1794.767                    4812.20930
## 3096                      1794.767                    4812.20930
## 3097                      8750.581                   13489.53488
## 3098                      8750.581                   13489.53488
## 3099                      8750.581                   13489.53488
## 3100                      8750.581                   13489.53488
## 3101                      8750.581                   13489.53488
## 3102                      8750.581                   13489.53488
## 3103                      8750.581                   13489.53488
## 3104                      8750.581                   13489.53488
## 3105                      8750.581                   13489.53488
## 3106                      8750.581                   13489.53488
## 3107                      8750.581                   13489.53488
## 3108                      8750.581                   13489.53488
## 3109                      8750.581                   13489.53488
## 3110                      8750.581                   13489.53488
## 3111                      8750.581                   13489.53488
## 3112                      8750.581                   13489.53488
## 3113                      8750.581                   13489.53488
## 3114                      8750.581                   13489.53488
## 3115                      8750.581                   13489.53488
## 3116                      8750.581                   13489.53488
## 3117                      8750.581                   13489.53488
## 3118                      8750.581                   13489.53488
## 3119                      8750.581                   13489.53488
## 3120                      8750.581                   13489.53488
## 3121                     12280.814                    5752.32558
## 3122                     12280.814                    5752.32558
## 3123                     12280.814                    5752.32558
## 3124                     12280.814                    5752.32558
## 3125                     12280.814                    5752.32558
## 3126                     12280.814                    5752.32558
## 3127                     12280.814                    5752.32558
## 3128                     12280.814                    5752.32558
## 3129                     12280.814                    5752.32558
## 3130                     12280.814                    5752.32558
## 3131                     12280.814                    5752.32558
## 3132                     12280.814                    5752.32558
## 3133                     12280.814                    5752.32558
## 3134                     12280.814                    5752.32558
## 3135                     12280.814                    5752.32558
## 3136                     12280.814                    5752.32558
## 3137                     12280.814                    5752.32558
## 3138                     12280.814                    5752.32558
## 3139                     12280.814                    5752.32558
## 3140                     12280.814                    5752.32558
## 3141                     12280.814                    5752.32558
## 3142                     12280.814                    5752.32558
## 3143                     12280.814                    5752.32558
## 3144                     12280.814                    5752.32558
## 3145                     15790.116                    4046.51163
## 3146                     15790.116                    4046.51163
## 3147                     15790.116                    4046.51163
## 3148                     15790.116                    4046.51163
## 3149                     15790.116                    4046.51163
## 3150                     15790.116                    4046.51163
## 3151                     15790.116                    4046.51163
## 3152                     15790.116                    4046.51163
## 3153                     15790.116                    4046.51163
## 3154                     15790.116                    4046.51163
## 3155                     15790.116                    4046.51163
## 3156                     15790.116                    4046.51163
## 3157                     15790.116                    4046.51163
## 3158                     15790.116                    4046.51163
## 3159                     15790.116                    4046.51163
## 3160                     15790.116                    4046.51163
## 3161                     15790.116                    4046.51163
## 3162                     15790.116                    4046.51163
## 3163                     15790.116                    4046.51163
## 3164                     15790.116                    4046.51163
## 3165                     15790.116                    4046.51163
## 3166                     15790.116                    4046.51163
## 3167                     15790.116                    4046.51163
## 3168                     15790.116                    4046.51163
## 3169                     12861.628                     568.60465
## 3170                     12861.628                     568.60465
## 3171                     12861.628                     568.60465
## 3172                     12861.628                     568.60465
## 3173                     12861.628                     568.60465
## 3174                     12861.628                     568.60465
## 3175                     12861.628                     568.60465
## 3176                     12861.628                     568.60465
## 3177                     12861.628                     568.60465
## 3178                     12861.628                     568.60465
## 3179                     12861.628                     568.60465
## 3180                     12861.628                     568.60465
## 3181                     12861.628                     568.60465
## 3182                     12861.628                     568.60465
## 3183                     12861.628                     568.60465
## 3184                     12861.628                     568.60465
## 3185                     12861.628                     568.60465
## 3186                     12861.628                     568.60465
## 3187                     12861.628                     568.60465
## 3188                     12861.628                     568.60465
## 3189                     12861.628                     568.60465
## 3190                     12861.628                     568.60465
## 3191                     12861.628                     568.60465
## 3192                     12861.628                     568.60465
## 3193                      7986.628                     837.20930
## 3194                      7986.628                     837.20930
## 3195                      7986.628                     837.20930
## 3196                      7986.628                     837.20930
## 3197                      7986.628                     837.20930
## 3198                      7986.628                     837.20930
## 3199                      7986.628                     837.20930
## 3200                      7986.628                     837.20930
## 3201                      7986.628                     837.20930
## 3202                      7986.628                     837.20930
## 3203                      7986.628                     837.20930
## 3204                      7986.628                     837.20930
## 3205                      7986.628                     837.20930
## 3206                      7986.628                     837.20930
## 3207                      7986.628                     837.20930
## 3208                      7986.628                     837.20930
## 3209                      7986.628                     837.20930
## 3210                      7986.628                     837.20930
## 3211                      7986.628                     837.20930
## 3212                      7986.628                     837.20930
## 3213                      7986.628                     837.20930
## 3214                      7986.628                     837.20930
## 3215                      7986.628                     837.20930
## 3216                      7986.628                     837.20930
## 3217                     12342.733                       0.00000
## 3218                     12342.733                       0.00000
## 3219                     12342.733                       0.00000
## 3220                     12342.733                       0.00000
## 3221                     12342.733                       0.00000
## 3222                     12342.733                       0.00000
## 3223                     12342.733                       0.00000
## 3224                     12342.733                       0.00000
## 3225                     12342.733                       0.00000
##      total_metaecosystem_Eug_indiv total_metaecosystem_Eup_indiv
## 1                       7834.88372                     3155.2326
## 2                       7834.88372                     3155.2326
## 3                       7834.88372                     3155.2326
## 4                       7834.88372                     3155.2326
## 5                       7834.88372                     3155.2326
## 6                       7834.88372                     3155.2326
## 7                       7834.88372                     3155.2326
## 8                       7834.88372                     3155.2326
## 9                       7834.88372                     3155.2326
## 10                      7834.88372                     3155.2326
## 11                      7834.88372                     3155.2326
## 12                      7834.88372                     3155.2326
## 13                      7834.88372                     3155.2326
## 14                      7834.88372                     3155.2326
## 15                      7834.88372                     3155.2326
## 16                      7834.88372                     3155.2326
## 17                      7834.88372                     3155.2326
## 18                      7834.88372                     3155.2326
## 19                      7834.88372                     3155.2326
## 20                      7834.88372                     3155.2326
## 21                      7834.88372                     3155.2326
## 22                      7834.88372                     3155.2326
## 23                      7834.88372                     3155.2326
## 24                      7834.88372                     3155.2326
## 25                      3057.55814                     4484.3023
## 26                      3057.55814                     4484.3023
## 27                      3057.55814                     4484.3023
## 28                      3057.55814                     4484.3023
## 29                      3057.55814                     4484.3023
## 30                      3057.55814                     4484.3023
## 31                      3057.55814                     4484.3023
## 32                      3057.55814                     4484.3023
## 33                      3057.55814                     4484.3023
## 34                      3057.55814                     4484.3023
## 35                      3057.55814                     4484.3023
## 36                      3057.55814                     4484.3023
## 37                      3057.55814                     4484.3023
## 38                      3057.55814                     4484.3023
## 39                      3057.55814                     4484.3023
## 40                      3057.55814                     4484.3023
## 41                      3057.55814                     4484.3023
## 42                      3057.55814                     4484.3023
## 43                      3057.55814                     4484.3023
## 44                      3057.55814                     4484.3023
## 45                      3057.55814                     4484.3023
## 46                      3057.55814                     4484.3023
## 47                      3057.55814                     4484.3023
## 48                      3057.55814                     4484.3023
## 49                       218.02326                     1318.6047
## 50                       218.02326                     1318.6047
## 51                       218.02326                     1318.6047
## 52                       218.02326                     1318.6047
## 53                       218.02326                     1318.6047
## 54                       218.02326                     1318.6047
## 55                       218.02326                     1318.6047
## 56                       218.02326                     1318.6047
## 57                       218.02326                     1318.6047
## 58                       218.02326                     1318.6047
## 59                       218.02326                     1318.6047
## 60                       218.02326                     1318.6047
## 61                       218.02326                     1318.6047
## 62                       218.02326                     1318.6047
## 63                       218.02326                     1318.6047
## 64                       218.02326                     1318.6047
## 65                       218.02326                     1318.6047
## 66                       218.02326                     1318.6047
## 67                       218.02326                     1318.6047
## 68                       218.02326                     1318.6047
## 69                       218.02326                     1318.6047
## 70                       218.02326                     1318.6047
## 71                       218.02326                     1318.6047
## 72                       218.02326                     1318.6047
## 73                         0.00000                     5084.3023
## 74                         0.00000                     5084.3023
## 75                         0.00000                     5084.3023
## 76                         0.00000                     5084.3023
## 77                         0.00000                     5084.3023
## 78                         0.00000                     5084.3023
## 79                         0.00000                     5084.3023
## 80                         0.00000                     5084.3023
## 81                         0.00000                     5084.3023
## 82                         0.00000                     5084.3023
## 83                         0.00000                     5084.3023
## 84                         0.00000                     5084.3023
## 85                         0.00000                     5084.3023
## 86                         0.00000                     5084.3023
## 87                         0.00000                     5084.3023
## 88                         0.00000                     5084.3023
## 89                         0.00000                     5084.3023
## 90                         0.00000                     5084.3023
## 91                         0.00000                     5084.3023
## 92                         0.00000                     5084.3023
## 93                         0.00000                     5084.3023
## 94                         0.00000                     5084.3023
## 95                         0.00000                     5084.3023
## 96                         0.00000                     5084.3023
## 97                         0.00000                     5241.2791
## 98                         0.00000                     5241.2791
## 99                         0.00000                     5241.2791
## 100                        0.00000                     5241.2791
## 101                        0.00000                     5241.2791
## 102                        0.00000                     5241.2791
## 103                        0.00000                     5241.2791
## 104                        0.00000                     5241.2791
## 105                        0.00000                     5241.2791
## 106                        0.00000                     5241.2791
## 107                        0.00000                     5241.2791
## 108                        0.00000                     5241.2791
## 109                        0.00000                     5241.2791
## 110                        0.00000                     5241.2791
## 111                        0.00000                     5241.2791
## 112                        0.00000                     5241.2791
## 113                        0.00000                     5241.2791
## 114                        0.00000                     5241.2791
## 115                        0.00000                     5241.2791
## 116                        0.00000                     5241.2791
## 117                        0.00000                     5241.2791
## 118                        0.00000                     5241.2791
## 119                        0.00000                     5241.2791
## 120                        0.00000                     5241.2791
## 121                        0.00000                     2956.3953
## 122                        0.00000                     2956.3953
## 123                        0.00000                     2956.3953
## 124                        0.00000                     2956.3953
## 125                        0.00000                     2956.3953
## 126                        0.00000                     2956.3953
## 127                        0.00000                     2956.3953
## 128                        0.00000                     2956.3953
## 129                        0.00000                     2956.3953
## 130                        0.00000                     2956.3953
## 131                        0.00000                     2956.3953
## 132                        0.00000                     2956.3953
## 133                        0.00000                     2956.3953
## 134                        0.00000                     2956.3953
## 135                        0.00000                     2956.3953
## 136                        0.00000                     2956.3953
## 137                        0.00000                     2956.3953
## 138                        0.00000                     2956.3953
## 139                        0.00000                     2956.3953
## 140                        0.00000                     2956.3953
## 141                        0.00000                     2956.3953
## 142                        0.00000                     2956.3953
## 143                        0.00000                     2956.3953
## 144                        0.00000                     2956.3953
## 145                        0.00000                      662.7907
## 146                        0.00000                      662.7907
## 147                        0.00000                      662.7907
## 148                        0.00000                      662.7907
## 149                        0.00000                      662.7907
## 150                        0.00000                      662.7907
## 151                        0.00000                      662.7907
## 152                        0.00000                      662.7907
## 153                        0.00000                      662.7907
## 154                        0.00000                      662.7907
## 155                        0.00000                      662.7907
## 156                        0.00000                      662.7907
## 157                        0.00000                      662.7907
## 158                        0.00000                      662.7907
## 159                        0.00000                      662.7907
## 160                        0.00000                      662.7907
## 161                        0.00000                      662.7907
## 162                        0.00000                      662.7907
## 163                        0.00000                      662.7907
## 164                        0.00000                      662.7907
## 165                        0.00000                      662.7907
## 166                        0.00000                      662.7907
## 167                        0.00000                      662.7907
## 168                        0.00000                      662.7907
## 169                     1090.11628                     2428.7791
## 170                     1090.11628                     2428.7791
## 171                     1090.11628                     2428.7791
## 172                     1090.11628                     2428.7791
## 173                     1090.11628                     2428.7791
## 174                     1090.11628                     2428.7791
## 175                     1090.11628                     2428.7791
## 176                     1090.11628                     2428.7791
## 177                     1090.11628                     2428.7791
## 178                     1090.11628                     2428.7791
## 179                     1090.11628                     2428.7791
## 180                     1090.11628                     2428.7791
## 181                     1090.11628                     2428.7791
## 182                     1090.11628                     2428.7791
## 183                     1090.11628                     2428.7791
## 184                     1090.11628                     2428.7791
## 185                     1090.11628                     2428.7791
## 186                     1090.11628                     2428.7791
## 187                     1090.11628                     2428.7791
## 188                     1090.11628                     2428.7791
## 189                     1090.11628                     2428.7791
## 190                     1090.11628                     2428.7791
## 191                     1090.11628                     2428.7791
## 192                     1090.11628                     2428.7791
## 193                     7834.88372                     3155.2326
## 194                     7834.88372                     3155.2326
## 195                     7834.88372                     3155.2326
## 196                     7834.88372                     3155.2326
## 197                     7834.88372                     3155.2326
## 198                     7834.88372                     3155.2326
## 199                     7834.88372                     3155.2326
## 200                     7834.88372                     3155.2326
## 201                     7834.88372                     3155.2326
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## 2795                     650.58140                     3453.4884
## 2796                     650.58140                     3453.4884
## 2797                     650.58140                     3453.4884
## 2798                     650.58140                     3453.4884
## 2799                     650.58140                     3453.4884
## 2800                     650.58140                     3453.4884
## 2801                     650.58140                     3453.4884
## 2802                     650.58140                     3453.4884
## 2803                     650.58140                     3453.4884
## 2804                     650.58140                     3453.4884
## 2805                     650.58140                     3453.4884
## 2806                     650.58140                     3453.4884
## 2807                     650.58140                     3453.4884
## 2808                     650.58140                     3453.4884
## 2809                       0.00000                     2581.3953
## 2810                       0.00000                     2581.3953
## 2811                       0.00000                     2581.3953
## 2812                       0.00000                     2581.3953
## 2813                       0.00000                     2581.3953
## 2814                       0.00000                     2581.3953
## 2815                       0.00000                     2581.3953
## 2816                       0.00000                     2581.3953
## 2817                       0.00000                     2581.3953
## 2818                       0.00000                     2581.3953
## 2819                       0.00000                     2581.3953
## 2820                       0.00000                     2581.3953
## 2821                       0.00000                     2581.3953
## 2822                       0.00000                     2581.3953
## 2823                       0.00000                     2581.3953
## 2824                       0.00000                     2581.3953
## 2825                       0.00000                     2581.3953
## 2826                       0.00000                     2581.3953
## 2827                       0.00000                     2581.3953
## 2828                       0.00000                     2581.3953
## 2829                       0.00000                     2581.3953
## 2830                       0.00000                     2581.3953
## 2831                       0.00000                     2581.3953
## 2832                       0.00000                     2581.3953
## 2833                    1609.01163                     1046.5116
## 2834                    1609.01163                     1046.5116
## 2835                    1609.01163                     1046.5116
## 2836                    1609.01163                     1046.5116
## 2837                    1609.01163                     1046.5116
## 2838                    1609.01163                     1046.5116
## 2839                    1609.01163                     1046.5116
## 2840                    1609.01163                     1046.5116
## 2841                    1609.01163                     1046.5116
## 2842                    1609.01163                     1046.5116
## 2843                    1609.01163                     1046.5116
## 2844                    1609.01163                     1046.5116
## 2845                    1609.01163                     1046.5116
## 2846                    1609.01163                     1046.5116
## 2847                    1609.01163                     1046.5116
## 2848                    1609.01163                     1046.5116
## 2849                    1609.01163                     1046.5116
## 2850                    1609.01163                     1046.5116
## 2851                    1609.01163                     1046.5116
## 2852                    1609.01163                     1046.5116
## 2853                    1609.01163                     1046.5116
## 2854                    1609.01163                     1046.5116
## 2855                    1609.01163                     1046.5116
## 2856                    1609.01163                     1046.5116
## 2857                     837.20930                     1090.1163
## 2858                     837.20930                     1090.1163
## 2859                     837.20930                     1090.1163
## 2860                     837.20930                     1090.1163
## 2861                     837.20930                     1090.1163
## 2862                     837.20930                     1090.1163
## 2863                     837.20930                     1090.1163
## 2864                     837.20930                     1090.1163
## 2865                     837.20930                     1090.1163
## 2866                     837.20930                     1090.1163
## 2867                     837.20930                     1090.1163
## 2868                     837.20930                     1090.1163
## 2869                     837.20930                     1090.1163
## 2870                     837.20930                     1090.1163
## 2871                     837.20930                     1090.1163
## 2872                     837.20930                     1090.1163
## 2873                     837.20930                     1090.1163
## 2874                     837.20930                     1090.1163
## 2875                     837.20930                     1090.1163
## 2876                     837.20930                     1090.1163
## 2877                     837.20930                     1090.1163
## 2878                     837.20930                     1090.1163
## 2879                     837.20930                     1090.1163
## 2880                     837.20930                     1090.1163
## 2881                    7834.88372                     3155.2326
## 2882                    7834.88372                     3155.2326
## 2883                    7834.88372                     3155.2326
## 2884                    7834.88372                     3155.2326
## 2885                    7834.88372                     3155.2326
## 2886                    7834.88372                     3155.2326
## 2887                    7834.88372                     3155.2326
## 2888                    7834.88372                     3155.2326
## 2889                    7834.88372                     3155.2326
## 2890                    7834.88372                     3155.2326
## 2891                    7834.88372                     3155.2326
## 2892                    7834.88372                     3155.2326
## 2893                    7834.88372                     3155.2326
## 2894                    7834.88372                     3155.2326
## 2895                    7834.88372                     3155.2326
## 2896                    7834.88372                     3155.2326
## 2897                    7834.88372                     3155.2326
## 2898                    7834.88372                     3155.2326
## 2899                    7834.88372                     3155.2326
## 2900                    7834.88372                     3155.2326
## 2901                    7834.88372                     3155.2326
## 2902                    7834.88372                     3155.2326
## 2903                    7834.88372                     3155.2326
## 2904                    7834.88372                     3155.2326
## 2905                    2398.25581                     3811.0465
## 2906                    2398.25581                     3811.0465
## 2907                    2398.25581                     3811.0465
## 2908                    2398.25581                     3811.0465
## 2909                    2398.25581                     3811.0465
## 2910                    2398.25581                     3811.0465
## 2911                    2398.25581                     3811.0465
## 2912                    2398.25581                     3811.0465
## 2913                    2398.25581                     3811.0465
## 2914                    2398.25581                     3811.0465
## 2915                    2398.25581                     3811.0465
## 2916                    2398.25581                     3811.0465
## 2917                    2398.25581                     3811.0465
## 2918                    2398.25581                     3811.0465
## 2919                    2398.25581                     3811.0465
## 2920                    2398.25581                     3811.0465
## 2921                    2398.25581                     3811.0465
## 2922                    2398.25581                     3811.0465
## 2923                    2398.25581                     3811.0465
## 2924                    2398.25581                     3811.0465
## 2925                    2398.25581                     3811.0465
## 2926                    2398.25581                     3811.0465
## 2927                    2398.25581                     3811.0465
## 2928                    2398.25581                     3811.0465
## 2929                     127.32558                     1887.2093
## 2930                     127.32558                     1887.2093
## 2931                     127.32558                     1887.2093
## 2932                     127.32558                     1887.2093
## 2933                     127.32558                     1887.2093
## 2934                     127.32558                     1887.2093
## 2935                     127.32558                     1887.2093
## 2936                     127.32558                     1887.2093
## 2937                     127.32558                     1887.2093
## 2938                     127.32558                     1887.2093
## 2939                     127.32558                     1887.2093
## 2940                     127.32558                     1887.2093
## 2941                     127.32558                     1887.2093
## 2942                     127.32558                     1887.2093
## 2943                     127.32558                     1887.2093
## 2944                     127.32558                     1887.2093
## 2945                     127.32558                     1887.2093
## 2946                     127.32558                     1887.2093
## 2947                     127.32558                     1887.2093
## 2948                     127.32558                     1887.2093
## 2949                     127.32558                     1887.2093
## 2950                     127.32558                     1887.2093
## 2951                     127.32558                     1887.2093
## 2952                     127.32558                     1887.2093
## 2953                       0.00000                     5084.3023
## 2954                       0.00000                     5084.3023
## 2955                       0.00000                     5084.3023
## 2956                       0.00000                     5084.3023
## 2957                       0.00000                     5084.3023
## 2958                       0.00000                     5084.3023
## 2959                       0.00000                     5084.3023
## 2960                       0.00000                     5084.3023
## 2961                       0.00000                     5084.3023
## 2962                       0.00000                     5084.3023
## 2963                       0.00000                     5084.3023
## 2964                       0.00000                     5084.3023
## 2965                       0.00000                     5084.3023
## 2966                       0.00000                     5084.3023
## 2967                       0.00000                     5084.3023
## 2968                       0.00000                     5084.3023
## 2969                       0.00000                     5084.3023
## 2970                       0.00000                     5084.3023
## 2971                       0.00000                     5084.3023
## 2972                       0.00000                     5084.3023
## 2973                       0.00000                     5084.3023
## 2974                       0.00000                     5084.3023
## 2975                       0.00000                     5084.3023
## 2976                       0.00000                     5084.3023
## 2977                       0.00000                     4822.6744
## 2978                       0.00000                     4822.6744
## 2979                       0.00000                     4822.6744
## 2980                       0.00000                     4822.6744
## 2981                       0.00000                     4822.6744
## 2982                       0.00000                     4822.6744
## 2983                       0.00000                     4822.6744
## 2984                       0.00000                     4822.6744
## 2985                       0.00000                     4822.6744
## 2986                       0.00000                     4822.6744
## 2987                       0.00000                     4822.6744
## 2988                       0.00000                     4822.6744
## 2989                       0.00000                     4822.6744
## 2990                       0.00000                     4822.6744
## 2991                       0.00000                     4822.6744
## 2992                       0.00000                     4822.6744
## 2993                       0.00000                     4822.6744
## 2994                       0.00000                     4822.6744
## 2995                       0.00000                     4822.6744
## 2996                       0.00000                     4822.6744
## 2997                       0.00000                     4822.6744
## 2998                       0.00000                     4822.6744
## 2999                       0.00000                     4822.6744
## 3000                       0.00000                     4822.6744
## 3001                       0.00000                     3174.4186
## 3002                       0.00000                     3174.4186
## 3003                       0.00000                     3174.4186
## 3004                       0.00000                     3174.4186
## 3005                       0.00000                     3174.4186
## 3006                       0.00000                     3174.4186
## 3007                       0.00000                     3174.4186
## 3008                       0.00000                     3174.4186
## 3009                       0.00000                     3174.4186
## 3010                       0.00000                     3174.4186
## 3011                       0.00000                     3174.4186
## 3012                       0.00000                     3174.4186
## 3013                       0.00000                     3174.4186
## 3014                       0.00000                     3174.4186
## 3015                       0.00000                     3174.4186
## 3016                       0.00000                     3174.4186
## 3017                       0.00000                     3174.4186
## 3018                       0.00000                     3174.4186
## 3019                       0.00000                     3174.4186
## 3020                       0.00000                     3174.4186
## 3021                       0.00000                     3174.4186
## 3022                       0.00000                     3174.4186
## 3023                       0.00000                     3174.4186
## 3024                       0.00000                     3174.4186
## 3025                       0.00000                      662.7907
## 3026                       0.00000                      662.7907
## 3027                       0.00000                      662.7907
## 3028                       0.00000                      662.7907
## 3029                       0.00000                      662.7907
## 3030                       0.00000                      662.7907
## 3031                       0.00000                      662.7907
## 3032                       0.00000                      662.7907
## 3033                       0.00000                      662.7907
## 3034                       0.00000                      662.7907
## 3035                       0.00000                      662.7907
## 3036                       0.00000                      662.7907
## 3037                       0.00000                      662.7907
## 3038                       0.00000                      662.7907
## 3039                       0.00000                      662.7907
## 3040                       0.00000                      662.7907
## 3041                       0.00000                      662.7907
## 3042                       0.00000                      662.7907
## 3043                       0.00000                      662.7907
## 3044                       0.00000                      662.7907
## 3045                       0.00000                      662.7907
## 3046                       0.00000                      662.7907
## 3047                       0.00000                      662.7907
## 3048                       0.00000                      662.7907
## 3049                    1090.11628                     2646.8023
## 3050                    1090.11628                     2646.8023
## 3051                    1090.11628                     2646.8023
## 3052                    1090.11628                     2646.8023
## 3053                    1090.11628                     2646.8023
## 3054                    1090.11628                     2646.8023
## 3055                    1090.11628                     2646.8023
## 3056                    1090.11628                     2646.8023
## 3057                    1090.11628                     2646.8023
## 3058                    1090.11628                     2646.8023
## 3059                    1090.11628                     2646.8023
## 3060                    1090.11628                     2646.8023
## 3061                    1090.11628                     2646.8023
## 3062                    1090.11628                     2646.8023
## 3063                    1090.11628                     2646.8023
## 3064                    1090.11628                     2646.8023
## 3065                    1090.11628                     2646.8023
## 3066                    1090.11628                     2646.8023
## 3067                    1090.11628                     2646.8023
## 3068                    1090.11628                     2646.8023
## 3069                    1090.11628                     2646.8023
## 3070                    1090.11628                     2646.8023
## 3071                    1090.11628                     2646.8023
## 3072                    1090.11628                     2646.8023
## 3073                    7834.88372                     3155.2326
## 3074                    7834.88372                     3155.2326
## 3075                    7834.88372                     3155.2326
## 3076                    7834.88372                     3155.2326
## 3077                    7834.88372                     3155.2326
## 3078                    7834.88372                     3155.2326
## 3079                    7834.88372                     3155.2326
## 3080                    7834.88372                     3155.2326
## 3081                    7834.88372                     3155.2326
## 3082                    7834.88372                     3155.2326
## 3083                    7834.88372                     3155.2326
## 3084                    7834.88372                     3155.2326
## 3085                    7834.88372                     3155.2326
## 3086                    7834.88372                     3155.2326
## 3087                    7834.88372                     3155.2326
## 3088                    7834.88372                     3155.2326
## 3089                    7834.88372                     3155.2326
## 3090                    7834.88372                     3155.2326
## 3091                    7834.88372                     3155.2326
## 3092                    7834.88372                     3155.2326
## 3093                    7834.88372                     3155.2326
## 3094                    7834.88372                     3155.2326
## 3095                    7834.88372                     3155.2326
## 3096                    7834.88372                     3155.2326
## 3097                    1281.97674                     7639.5349
## 3098                    1281.97674                     7639.5349
## 3099                    1281.97674                     7639.5349
## 3100                    1281.97674                     7639.5349
## 3101                    1281.97674                     7639.5349
## 3102                    1281.97674                     7639.5349
## 3103                    1281.97674                     7639.5349
## 3104                    1281.97674                     7639.5349
## 3105                    1281.97674                     7639.5349
## 3106                    1281.97674                     7639.5349
## 3107                    1281.97674                     7639.5349
## 3108                    1281.97674                     7639.5349
## 3109                    1281.97674                     7639.5349
## 3110                    1281.97674                     7639.5349
## 3111                    1281.97674                     7639.5349
## 3112                    1281.97674                     7639.5349
## 3113                    1281.97674                     7639.5349
## 3114                    1281.97674                     7639.5349
## 3115                    1281.97674                     7639.5349
## 3116                    1281.97674                     7639.5349
## 3117                    1281.97674                     7639.5349
## 3118                    1281.97674                     7639.5349
## 3119                    1281.97674                     7639.5349
## 3120                    1281.97674                     7639.5349
## 3121                     127.32558                     3640.1163
## 3122                     127.32558                     3640.1163
## 3123                     127.32558                     3640.1163
## 3124                     127.32558                     3640.1163
## 3125                     127.32558                     3640.1163
## 3126                     127.32558                     3640.1163
## 3127                     127.32558                     3640.1163
## 3128                     127.32558                     3640.1163
## 3129                     127.32558                     3640.1163
## 3130                     127.32558                     3640.1163
## 3131                     127.32558                     3640.1163
## 3132                     127.32558                     3640.1163
## 3133                     127.32558                     3640.1163
## 3134                     127.32558                     3640.1163
## 3135                     127.32558                     3640.1163
## 3136                     127.32558                     3640.1163
## 3137                     127.32558                     3640.1163
## 3138                     127.32558                     3640.1163
## 3139                     127.32558                     3640.1163
## 3140                     127.32558                     3640.1163
## 3141                     127.32558                     3640.1163
## 3142                     127.32558                     3640.1163
## 3143                     127.32558                     3640.1163
## 3144                     127.32558                     3640.1163
## 3145                    1438.95349                     4988.3721
## 3146                    1438.95349                     4988.3721
## 3147                    1438.95349                     4988.3721
## 3148                    1438.95349                     4988.3721
## 3149                    1438.95349                     4988.3721
## 3150                    1438.95349                     4988.3721
## 3151                    1438.95349                     4988.3721
## 3152                    1438.95349                     4988.3721
## 3153                    1438.95349                     4988.3721
## 3154                    1438.95349                     4988.3721
## 3155                    1438.95349                     4988.3721
## 3156                    1438.95349                     4988.3721
## 3157                    1438.95349                     4988.3721
## 3158                    1438.95349                     4988.3721
## 3159                    1438.95349                     4988.3721
## 3160                    1438.95349                     4988.3721
## 3161                    1438.95349                     4988.3721
## 3162                    1438.95349                     4988.3721
## 3163                    1438.95349                     4988.3721
## 3164                    1438.95349                     4988.3721
## 3165                    1438.95349                     4988.3721
## 3166                    1438.95349                     4988.3721
## 3167                    1438.95349                     4988.3721
## 3168                    1438.95349                     4988.3721
## 3169                       0.00000                     4116.2791
## 3170                       0.00000                     4116.2791
## 3171                       0.00000                     4116.2791
## 3172                       0.00000                     4116.2791
## 3173                       0.00000                     4116.2791
## 3174                       0.00000                     4116.2791
## 3175                       0.00000                     4116.2791
## 3176                       0.00000                     4116.2791
## 3177                       0.00000                     4116.2791
## 3178                       0.00000                     4116.2791
## 3179                       0.00000                     4116.2791
## 3180                       0.00000                     4116.2791
## 3181                       0.00000                     4116.2791
## 3182                       0.00000                     4116.2791
## 3183                       0.00000                     4116.2791
## 3184                       0.00000                     4116.2791
## 3185                       0.00000                     4116.2791
## 3186                       0.00000                     4116.2791
## 3187                       0.00000                     4116.2791
## 3188                       0.00000                     4116.2791
## 3189                       0.00000                     4116.2791
## 3190                       0.00000                     4116.2791
## 3191                       0.00000                     4116.2791
## 3192                       0.00000                     4116.2791
## 3193                       0.00000                     1674.4186
## 3194                       0.00000                     1674.4186
## 3195                       0.00000                     1674.4186
## 3196                       0.00000                     1674.4186
## 3197                       0.00000                     1674.4186
## 3198                       0.00000                     1674.4186
## 3199                       0.00000                     1674.4186
## 3200                       0.00000                     1674.4186
## 3201                       0.00000                     1674.4186
## 3202                       0.00000                     1674.4186
## 3203                       0.00000                     1674.4186
## 3204                       0.00000                     1674.4186
## 3205                       0.00000                     1674.4186
## 3206                       0.00000                     1674.4186
## 3207                       0.00000                     1674.4186
## 3208                       0.00000                     1674.4186
## 3209                       0.00000                     1674.4186
## 3210                       0.00000                     1674.4186
## 3211                       0.00000                     1674.4186
## 3212                       0.00000                     1674.4186
## 3213                       0.00000                     1674.4186
## 3214                       0.00000                     1674.4186
## 3215                       0.00000                     1674.4186
## 3216                       0.00000                     1674.4186
## 3217                    1312.50000                     1059.5930
## 3218                    1312.50000                     1059.5930
## 3219                    1312.50000                     1059.5930
## 3220                    1312.50000                     1059.5930
## 3221                    1312.50000                     1059.5930
## 3222                    1312.50000                     1059.5930
## 3223                    1312.50000                     1059.5930
## 3224                    1312.50000                     1059.5930
## 3225                    1312.50000                     1059.5930
##      total_metaecosystem_Lox_indiv total_metaecosystem_Pau_indiv
## 1                        2494.1860                     5161.0465
## 2                        2494.1860                     5161.0465
## 3                        2494.1860                     5161.0465
## 4                        2494.1860                     5161.0465
## 5                        2494.1860                     5161.0465
## 6                        2494.1860                     5161.0465
## 7                        2494.1860                     5161.0465
## 8                        2494.1860                     5161.0465
## 9                        2494.1860                     5161.0465
## 10                       2494.1860                     5161.0465
## 11                       2494.1860                     5161.0465
## 12                       2494.1860                     5161.0465
## 13                       2494.1860                     5161.0465
## 14                       2494.1860                     5161.0465
## 15                       2494.1860                     5161.0465
## 16                       2494.1860                     5161.0465
## 17                       2494.1860                     5161.0465
## 18                       2494.1860                     5161.0465
## 19                       2494.1860                     5161.0465
## 20                       2494.1860                     5161.0465
## 21                       2494.1860                     5161.0465
## 22                       2494.1860                     5161.0465
## 23                       2494.1860                     5161.0465
## 24                       2494.1860                     5161.0465
## 25                        218.0233                        0.0000
## 26                        218.0233                        0.0000
## 27                        218.0233                        0.0000
## 28                        218.0233                        0.0000
## 29                        218.0233                        0.0000
## 30                        218.0233                        0.0000
## 31                        218.0233                        0.0000
## 32                        218.0233                        0.0000
## 33                        218.0233                        0.0000
## 34                        218.0233                        0.0000
## 35                        218.0233                        0.0000
## 36                        218.0233                        0.0000
## 37                        218.0233                        0.0000
## 38                        218.0233                        0.0000
## 39                        218.0233                        0.0000
## 40                        218.0233                        0.0000
## 41                        218.0233                        0.0000
## 42                        218.0233                        0.0000
## 43                        218.0233                        0.0000
## 44                        218.0233                        0.0000
## 45                        218.0233                        0.0000
## 46                        218.0233                        0.0000
## 47                        218.0233                        0.0000
## 48                        218.0233                        0.0000
## 49                          0.0000                     1970.9302
## 50                          0.0000                     1970.9302
## 51                          0.0000                     1970.9302
## 52                          0.0000                     1970.9302
## 53                          0.0000                     1970.9302
## 54                          0.0000                     1970.9302
## 55                          0.0000                     1970.9302
## 56                          0.0000                     1970.9302
## 57                          0.0000                     1970.9302
## 58                          0.0000                     1970.9302
## 59                          0.0000                     1970.9302
## 60                          0.0000                     1970.9302
## 61                          0.0000                     1970.9302
## 62                          0.0000                     1970.9302
## 63                          0.0000                     1970.9302
## 64                          0.0000                     1970.9302
## 65                          0.0000                     1970.9302
## 66                          0.0000                     1970.9302
## 67                          0.0000                     1970.9302
## 68                          0.0000                     1970.9302
## 69                          0.0000                     1970.9302
## 70                          0.0000                     1970.9302
## 71                          0.0000                     1970.9302
## 72                          0.0000                     1970.9302
## 73                          0.0000                     1369.1860
## 74                          0.0000                     1369.1860
## 75                          0.0000                     1369.1860
## 76                          0.0000                     1369.1860
## 77                          0.0000                     1369.1860
## 78                          0.0000                     1369.1860
## 79                          0.0000                     1369.1860
## 80                          0.0000                     1369.1860
## 81                          0.0000                     1369.1860
## 82                          0.0000                     1369.1860
## 83                          0.0000                     1369.1860
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## 1728                      545.0581                      436.0465
## 1729                     2494.1860                     5161.0465
## 1730                     2494.1860                     5161.0465
## 1731                     2494.1860                     5161.0465
## 1732                     2494.1860                     5161.0465
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## 1736                     2494.1860                     5161.0465
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## 1741                     2494.1860                     5161.0465
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## 1744                     2494.1860                     5161.0465
## 1745                     2494.1860                     5161.0465
## 1746                     2494.1860                     5161.0465
## 1747                     2494.1860                     5161.0465
## 1748                     2494.1860                     5161.0465
## 1749                     2494.1860                     5161.0465
## 1750                     2494.1860                     5161.0465
## 1751                     2494.1860                     5161.0465
## 1752                     2494.1860                     5161.0465
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## 1754                     2283.1395                     4890.6977
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## 1760                     2283.1395                     4890.6977
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## 1776                     2283.1395                     4890.6977
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## 1778                      282.5581                      570.3488
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## 1903                      545.0581                      453.4884
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## 1905                      545.0581                      453.4884
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## 1914                      545.0581                      453.4884
## 1915                      545.0581                      453.4884
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## 1943                     2494.1860                     5161.0465
## 1944                     2494.1860                     5161.0465
## 1945                        0.0000                        0.0000
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## 1994                        0.0000                     1852.3256
## 1995                        0.0000                     1852.3256
## 1996                        0.0000                     1852.3256
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## 1998                        0.0000                     1852.3256
## 1999                        0.0000                     1852.3256
## 2000                        0.0000                     1852.3256
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## 2003                        0.0000                     1852.3256
## 2004                        0.0000                     1852.3256
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## 2006                        0.0000                     1852.3256
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## 2010                        0.0000                     1852.3256
## 2011                        0.0000                     1852.3256
## 2012                        0.0000                     1852.3256
## 2013                        0.0000                     1852.3256
## 2014                        0.0000                     1852.3256
## 2015                        0.0000                     1852.3256
## 2016                        0.0000                     1852.3256
## 2017                        0.0000                     2572.6744
## 2018                        0.0000                     2572.6744
## 2019                        0.0000                     2572.6744
## 2020                        0.0000                     2572.6744
## 2021                        0.0000                     2572.6744
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## 2140                     1220.9302                     5720.9302
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## 2788                     1090.1163                     1735.4651
## 2789                     1090.1163                     1735.4651
## 2790                     1090.1163                     1735.4651
## 2791                     1090.1163                     1735.4651
## 2792                     1090.1163                     1735.4651
## 2793                     1090.1163                     1735.4651
## 2794                     1090.1163                     1735.4651
## 2795                     1090.1163                     1735.4651
## 2796                     1090.1163                     1735.4651
## 2797                     1090.1163                     1735.4651
## 2798                     1090.1163                     1735.4651
## 2799                     1090.1163                     1735.4651
## 2800                     1090.1163                     1735.4651
## 2801                     1090.1163                     1735.4651
## 2802                     1090.1163                     1735.4651
## 2803                     1090.1163                     1735.4651
## 2804                     1090.1163                     1735.4651
## 2805                     1090.1163                     1735.4651
## 2806                     1090.1163                     1735.4651
## 2807                     1090.1163                     1735.4651
## 2808                     1090.1163                     1735.4651
## 2809                     1875.0000                     1822.6744
## 2810                     1875.0000                     1822.6744
## 2811                     1875.0000                     1822.6744
## 2812                     1875.0000                     1822.6744
## 2813                     1875.0000                     1822.6744
## 2814                     1875.0000                     1822.6744
## 2815                     1875.0000                     1822.6744
## 2816                     1875.0000                     1822.6744
## 2817                     1875.0000                     1822.6744
## 2818                     1875.0000                     1822.6744
## 2819                     1875.0000                     1822.6744
## 2820                     1875.0000                     1822.6744
## 2821                     1875.0000                     1822.6744
## 2822                     1875.0000                     1822.6744
## 2823                     1875.0000                     1822.6744
## 2824                     1875.0000                     1822.6744
## 2825                     1875.0000                     1822.6744
## 2826                     1875.0000                     1822.6744
## 2827                     1875.0000                     1822.6744
## 2828                     1875.0000                     1822.6744
## 2829                     1875.0000                     1822.6744
## 2830                     1875.0000                     1822.6744
## 2831                     1875.0000                     1822.6744
## 2832                     1875.0000                     1822.6744
## 2833                     1521.8023                        0.0000
## 2834                     1521.8023                        0.0000
## 2835                     1521.8023                        0.0000
## 2836                     1521.8023                        0.0000
## 2837                     1521.8023                        0.0000
## 2838                     1521.8023                        0.0000
## 2839                     1521.8023                        0.0000
## 2840                     1521.8023                        0.0000
## 2841                     1521.8023                        0.0000
## 2842                     1521.8023                        0.0000
## 2843                     1521.8023                        0.0000
## 2844                     1521.8023                        0.0000
## 2845                     1521.8023                        0.0000
## 2846                     1521.8023                        0.0000
## 2847                     1521.8023                        0.0000
## 2848                     1521.8023                        0.0000
## 2849                     1521.8023                        0.0000
## 2850                     1521.8023                        0.0000
## 2851                     1521.8023                        0.0000
## 2852                     1521.8023                        0.0000
## 2853                     1521.8023                        0.0000
## 2854                     1521.8023                        0.0000
## 2855                     1521.8023                        0.0000
## 2856                     1521.8023                        0.0000
## 2857                      545.0581                      453.4884
## 2858                      545.0581                      453.4884
## 2859                      545.0581                      453.4884
## 2860                      545.0581                      453.4884
## 2861                      545.0581                      453.4884
## 2862                      545.0581                      453.4884
## 2863                      545.0581                      453.4884
## 2864                      545.0581                      453.4884
## 2865                      545.0581                      453.4884
## 2866                      545.0581                      453.4884
## 2867                      545.0581                      453.4884
## 2868                      545.0581                      453.4884
## 2869                      545.0581                      453.4884
## 2870                      545.0581                      453.4884
## 2871                      545.0581                      453.4884
## 2872                      545.0581                      453.4884
## 2873                      545.0581                      453.4884
## 2874                      545.0581                      453.4884
## 2875                      545.0581                      453.4884
## 2876                      545.0581                      453.4884
## 2877                      545.0581                      453.4884
## 2878                      545.0581                      453.4884
## 2879                      545.0581                      453.4884
## 2880                      545.0581                      453.4884
## 2881                     2494.1860                     5161.0465
## 2882                     2494.1860                     5161.0465
## 2883                     2494.1860                     5161.0465
## 2884                     2494.1860                     5161.0465
## 2885                     2494.1860                     5161.0465
## 2886                     2494.1860                     5161.0465
## 2887                     2494.1860                     5161.0465
## 2888                     2494.1860                     5161.0465
## 2889                     2494.1860                     5161.0465
## 2890                     2494.1860                     5161.0465
## 2891                     2494.1860                     5161.0465
## 2892                     2494.1860                     5161.0465
## 2893                     2494.1860                     5161.0465
## 2894                     2494.1860                     5161.0465
## 2895                     2494.1860                     5161.0465
## 2896                     2494.1860                     5161.0465
## 2897                     2494.1860                     5161.0465
## 2898                     2494.1860                     5161.0465
## 2899                     2494.1860                     5161.0465
## 2900                     2494.1860                     5161.0465
## 2901                     2494.1860                     5161.0465
## 2902                     2494.1860                     5161.0465
## 2903                     2494.1860                     5161.0465
## 2904                     2494.1860                     5161.0465
## 2905                        0.0000                      102.9070
## 2906                        0.0000                      102.9070
## 2907                        0.0000                      102.9070
## 2908                        0.0000                      102.9070
## 2909                        0.0000                      102.9070
## 2910                        0.0000                      102.9070
## 2911                        0.0000                      102.9070
## 2912                        0.0000                      102.9070
## 2913                        0.0000                      102.9070
## 2914                        0.0000                      102.9070
## 2915                        0.0000                      102.9070
## 2916                        0.0000                      102.9070
## 2917                        0.0000                      102.9070
## 2918                        0.0000                      102.9070
## 2919                        0.0000                      102.9070
## 2920                        0.0000                      102.9070
## 2921                        0.0000                      102.9070
## 2922                        0.0000                      102.9070
## 2923                        0.0000                      102.9070
## 2924                        0.0000                      102.9070
## 2925                        0.0000                      102.9070
## 2926                        0.0000                      102.9070
## 2927                        0.0000                      102.9070
## 2928                        0.0000                      102.9070
## 2929                      218.0233                     1838.3721
## 2930                      218.0233                     1838.3721
## 2931                      218.0233                     1838.3721
## 2932                      218.0233                     1838.3721
## 2933                      218.0233                     1838.3721
## 2934                      218.0233                     1838.3721
## 2935                      218.0233                     1838.3721
## 2936                      218.0233                     1838.3721
## 2937                      218.0233                     1838.3721
## 2938                      218.0233                     1838.3721
## 2939                      218.0233                     1838.3721
## 2940                      218.0233                     1838.3721
## 2941                      218.0233                     1838.3721
## 2942                      218.0233                     1838.3721
## 2943                      218.0233                     1838.3721
## 2944                      218.0233                     1838.3721
## 2945                      218.0233                     1838.3721
## 2946                      218.0233                     1838.3721
## 2947                      218.0233                     1838.3721
## 2948                      218.0233                     1838.3721
## 2949                      218.0233                     1838.3721
## 2950                      218.0233                     1838.3721
## 2951                      218.0233                     1838.3721
## 2952                      218.0233                     1838.3721
## 2953                        0.0000                     1369.1860
## 2954                        0.0000                     1369.1860
## 2955                        0.0000                     1369.1860
## 2956                        0.0000                     1369.1860
## 2957                        0.0000                     1369.1860
## 2958                        0.0000                     1369.1860
## 2959                        0.0000                     1369.1860
## 2960                        0.0000                     1369.1860
## 2961                        0.0000                     1369.1860
## 2962                        0.0000                     1369.1860
## 2963                        0.0000                     1369.1860
## 2964                        0.0000                     1369.1860
## 2965                        0.0000                     1369.1860
## 2966                        0.0000                     1369.1860
## 2967                        0.0000                     1369.1860
## 2968                        0.0000                     1369.1860
## 2969                        0.0000                     1369.1860
## 2970                        0.0000                     1369.1860
## 2971                        0.0000                     1369.1860
## 2972                        0.0000                     1369.1860
## 2973                        0.0000                     1369.1860
## 2974                        0.0000                     1369.1860
## 2975                        0.0000                     1369.1860
## 2976                        0.0000                     1369.1860
## 2977                        0.0000                     2532.5581
## 2978                        0.0000                     2532.5581
## 2979                        0.0000                     2532.5581
## 2980                        0.0000                     2532.5581
## 2981                        0.0000                     2532.5581
## 2982                        0.0000                     2532.5581
## 2983                        0.0000                     2532.5581
## 2984                        0.0000                     2532.5581
## 2985                        0.0000                     2532.5581
## 2986                        0.0000                     2532.5581
## 2987                        0.0000                     2532.5581
## 2988                        0.0000                     2532.5581
## 2989                        0.0000                     2532.5581
## 2990                        0.0000                     2532.5581
## 2991                        0.0000                     2532.5581
## 2992                        0.0000                     2532.5581
## 2993                        0.0000                     2532.5581
## 2994                        0.0000                     2532.5581
## 2995                        0.0000                     2532.5581
## 2996                        0.0000                     2532.5581
## 2997                        0.0000                     2532.5581
## 2998                        0.0000                     2532.5581
## 2999                        0.0000                     2532.5581
## 3000                        0.0000                     2532.5581
## 3001                        0.0000                      392.4419
## 3002                        0.0000                      392.4419
## 3003                        0.0000                      392.4419
## 3004                        0.0000                      392.4419
## 3005                        0.0000                      392.4419
## 3006                        0.0000                      392.4419
## 3007                        0.0000                      392.4419
## 3008                        0.0000                      392.4419
## 3009                        0.0000                      392.4419
## 3010                        0.0000                      392.4419
## 3011                        0.0000                      392.4419
## 3012                        0.0000                      392.4419
## 3013                        0.0000                      392.4419
## 3014                        0.0000                      392.4419
## 3015                        0.0000                      392.4419
## 3016                        0.0000                      392.4419
## 3017                        0.0000                      392.4419
## 3018                        0.0000                      392.4419
## 3019                        0.0000                      392.4419
## 3020                        0.0000                      392.4419
## 3021                        0.0000                      392.4419
## 3022                        0.0000                      392.4419
## 3023                        0.0000                      392.4419
## 3024                        0.0000                      392.4419
## 3025                      545.0581                      213.6628
## 3026                      545.0581                      213.6628
## 3027                      545.0581                      213.6628
## 3028                      545.0581                      213.6628
## 3029                      545.0581                      213.6628
## 3030                      545.0581                      213.6628
## 3031                      545.0581                      213.6628
## 3032                      545.0581                      213.6628
## 3033                      545.0581                      213.6628
## 3034                      545.0581                      213.6628
## 3035                      545.0581                      213.6628
## 3036                      545.0581                      213.6628
## 3037                      545.0581                      213.6628
## 3038                      545.0581                      213.6628
## 3039                      545.0581                      213.6628
## 3040                      545.0581                      213.6628
## 3041                      545.0581                      213.6628
## 3042                      545.0581                      213.6628
## 3043                      545.0581                      213.6628
## 3044                      545.0581                      213.6628
## 3045                      545.0581                      213.6628
## 3046                      545.0581                      213.6628
## 3047                      545.0581                      213.6628
## 3048                      545.0581                      213.6628
## 3049                     1630.8140                      504.0698
## 3050                     1630.8140                      504.0698
## 3051                     1630.8140                      504.0698
## 3052                     1630.8140                      504.0698
## 3053                     1630.8140                      504.0698
## 3054                     1630.8140                      504.0698
## 3055                     1630.8140                      504.0698
## 3056                     1630.8140                      504.0698
## 3057                     1630.8140                      504.0698
## 3058                     1630.8140                      504.0698
## 3059                     1630.8140                      504.0698
## 3060                     1630.8140                      504.0698
## 3061                     1630.8140                      504.0698
## 3062                     1630.8140                      504.0698
## 3063                     1630.8140                      504.0698
## 3064                     1630.8140                      504.0698
## 3065                     1630.8140                      504.0698
## 3066                     1630.8140                      504.0698
## 3067                     1630.8140                      504.0698
## 3068                     1630.8140                      504.0698
## 3069                     1630.8140                      504.0698
## 3070                     1630.8140                      504.0698
## 3071                     1630.8140                      504.0698
## 3072                     1630.8140                      504.0698
## 3073                     2494.1860                     5161.0465
## 3074                     2494.1860                     5161.0465
## 3075                     2494.1860                     5161.0465
## 3076                     2494.1860                     5161.0465
## 3077                     2494.1860                     5161.0465
## 3078                     2494.1860                     5161.0465
## 3079                     2494.1860                     5161.0465
## 3080                     2494.1860                     5161.0465
## 3081                     2494.1860                     5161.0465
## 3082                     2494.1860                     5161.0465
## 3083                     2494.1860                     5161.0465
## 3084                     2494.1860                     5161.0465
## 3085                     2494.1860                     5161.0465
## 3086                     2494.1860                     5161.0465
## 3087                     2494.1860                     5161.0465
## 3088                     2494.1860                     5161.0465
## 3089                     2494.1860                     5161.0465
## 3090                     2494.1860                     5161.0465
## 3091                     2494.1860                     5161.0465
## 3092                     2494.1860                     5161.0465
## 3093                     2494.1860                     5161.0465
## 3094                     2494.1860                     5161.0465
## 3095                     2494.1860                     5161.0465
## 3096                     2494.1860                     5161.0465
## 3097                     1220.9302                     5823.8372
## 3098                     1220.9302                     5823.8372
## 3099                     1220.9302                     5823.8372
## 3100                     1220.9302                     5823.8372
## 3101                     1220.9302                     5823.8372
## 3102                     1220.9302                     5823.8372
## 3103                     1220.9302                     5823.8372
## 3104                     1220.9302                     5823.8372
## 3105                     1220.9302                     5823.8372
## 3106                     1220.9302                     5823.8372
## 3107                     1220.9302                     5823.8372
## 3108                     1220.9302                     5823.8372
## 3109                     1220.9302                     5823.8372
## 3110                     1220.9302                     5823.8372
## 3111                     1220.9302                     5823.8372
## 3112                     1220.9302                     5823.8372
## 3113                     1220.9302                     5823.8372
## 3114                     1220.9302                     5823.8372
## 3115                     1220.9302                     5823.8372
## 3116                     1220.9302                     5823.8372
## 3117                     1220.9302                     5823.8372
## 3118                     1220.9302                     5823.8372
## 3119                     1220.9302                     5823.8372
## 3120                     1220.9302                     5823.8372
## 3121                      218.0233                      198.8372
## 3122                      218.0233                      198.8372
## 3123                      218.0233                      198.8372
## 3124                      218.0233                      198.8372
## 3125                      218.0233                      198.8372
## 3126                      218.0233                      198.8372
## 3127                      218.0233                      198.8372
## 3128                      218.0233                      198.8372
## 3129                      218.0233                      198.8372
## 3130                      218.0233                      198.8372
## 3131                      218.0233                      198.8372
## 3132                      218.0233                      198.8372
## 3133                      218.0233                      198.8372
## 3134                      218.0233                      198.8372
## 3135                      218.0233                      198.8372
## 3136                      218.0233                      198.8372
## 3137                      218.0233                      198.8372
## 3138                      218.0233                      198.8372
## 3139                      218.0233                      198.8372
## 3140                      218.0233                      198.8372
## 3141                      218.0233                      198.8372
## 3142                      218.0233                      198.8372
## 3143                      218.0233                      198.8372
## 3144                      218.0233                      198.8372
## 3145                        0.0000                     3052.3256
## 3146                        0.0000                     3052.3256
## 3147                        0.0000                     3052.3256
## 3148                        0.0000                     3052.3256
## 3149                        0.0000                     3052.3256
## 3150                        0.0000                     3052.3256
## 3151                        0.0000                     3052.3256
## 3152                        0.0000                     3052.3256
## 3153                        0.0000                     3052.3256
## 3154                        0.0000                     3052.3256
## 3155                        0.0000                     3052.3256
## 3156                        0.0000                     3052.3256
## 3157                        0.0000                     3052.3256
## 3158                        0.0000                     3052.3256
## 3159                        0.0000                     3052.3256
## 3160                        0.0000                     3052.3256
## 3161                        0.0000                     3052.3256
## 3162                        0.0000                     3052.3256
## 3163                        0.0000                     3052.3256
## 3164                        0.0000                     3052.3256
## 3165                        0.0000                     3052.3256
## 3166                        0.0000                     3052.3256
## 3167                        0.0000                     3052.3256
## 3168                        0.0000                     3052.3256
## 3169                        0.0000                     1948.2558
## 3170                        0.0000                     1948.2558
## 3171                        0.0000                     1948.2558
## 3172                        0.0000                     1948.2558
## 3173                        0.0000                     1948.2558
## 3174                        0.0000                     1948.2558
## 3175                        0.0000                     1948.2558
## 3176                        0.0000                     1948.2558
## 3177                        0.0000                     1948.2558
## 3178                        0.0000                     1948.2558
## 3179                        0.0000                     1948.2558
## 3180                        0.0000                     1948.2558
## 3181                        0.0000                     1948.2558
## 3182                        0.0000                     1948.2558
## 3183                        0.0000                     1948.2558
## 3184                        0.0000                     1948.2558
## 3185                        0.0000                     1948.2558
## 3186                        0.0000                     1948.2558
## 3187                        0.0000                     1948.2558
## 3188                        0.0000                     1948.2558
## 3189                        0.0000                     1948.2558
## 3190                        0.0000                     1948.2558
## 3191                        0.0000                     1948.2558
## 3192                        0.0000                     1948.2558
## 3193                        0.0000                     1665.6977
## 3194                        0.0000                     1665.6977
## 3195                        0.0000                     1665.6977
## 3196                        0.0000                     1665.6977
## 3197                        0.0000                     1665.6977
## 3198                        0.0000                     1665.6977
## 3199                        0.0000                     1665.6977
## 3200                        0.0000                     1665.6977
## 3201                        0.0000                     1665.6977
## 3202                        0.0000                     1665.6977
## 3203                        0.0000                     1665.6977
## 3204                        0.0000                     1665.6977
## 3205                        0.0000                     1665.6977
## 3206                        0.0000                     1665.6977
## 3207                        0.0000                     1665.6977
## 3208                        0.0000                     1665.6977
## 3209                        0.0000                     1665.6977
## 3210                        0.0000                     1665.6977
## 3211                        0.0000                     1665.6977
## 3212                        0.0000                     1665.6977
## 3213                        0.0000                     1665.6977
## 3214                        0.0000                     1665.6977
## 3215                        0.0000                     1665.6977
## 3216                        0.0000                     1665.6977
## 3217                        0.0000                     1125.0000
## 3218                        0.0000                     1125.0000
## 3219                        0.0000                     1125.0000
## 3220                        0.0000                     1125.0000
## 3221                        0.0000                     1125.0000
## 3222                        0.0000                     1125.0000
## 3223                        0.0000                     1125.0000
## 3224                        0.0000                     1125.0000
## 3225                        0.0000                     1125.0000
##      total_metaecosystem_Pca_indiv total_metaecosystem_Spi_indiv
## 1                        883.43023                     635.75581
## 2                        883.43023                     635.75581
## 3                        883.43023                     635.75581
## 4                        883.43023                     635.75581
## 5                        883.43023                     635.75581
## 6                        883.43023                     635.75581
## 7                        883.43023                     635.75581
## 8                        883.43023                     635.75581
## 9                        883.43023                     635.75581
## 10                       883.43023                     635.75581
## 11                       883.43023                     635.75581
## 12                       883.43023                     635.75581
## 13                       883.43023                     635.75581
## 14                       883.43023                     635.75581
## 15                       883.43023                     635.75581
## 16                       883.43023                     635.75581
## 17                       883.43023                     635.75581
## 18                       883.43023                     635.75581
## 19                       883.43023                     635.75581
## 20                       883.43023                     635.75581
## 21                       883.43023                     635.75581
## 22                       883.43023                     635.75581
## 23                       883.43023                     635.75581
## 24                       883.43023                     635.75581
## 25                      1854.06977                    1055.23256
## 26                      1854.06977                    1055.23256
## 27                      1854.06977                    1055.23256
## 28                      1854.06977                    1055.23256
## 29                      1854.06977                    1055.23256
## 30                      1854.06977                    1055.23256
## 31                      1854.06977                    1055.23256
## 32                      1854.06977                    1055.23256
## 33                      1854.06977                    1055.23256
## 34                      1854.06977                    1055.23256
## 35                      1854.06977                    1055.23256
## 36                      1854.06977                    1055.23256
## 37                      1854.06977                    1055.23256
## 38                      1854.06977                    1055.23256
## 39                      1854.06977                    1055.23256
## 40                      1854.06977                    1055.23256
## 41                      1854.06977                    1055.23256
## 42                      1854.06977                    1055.23256
## 43                      1854.06977                    1055.23256
## 44                      1854.06977                    1055.23256
## 45                      1854.06977                    1055.23256
## 46                      1854.06977                    1055.23256
## 47                      1854.06977                    1055.23256
## 48                      1854.06977                    1055.23256
## 49                      1189.53488                    1090.11628
## 50                      1189.53488                    1090.11628
## 51                      1189.53488                    1090.11628
## 52                      1189.53488                    1090.11628
## 53                      1189.53488                    1090.11628
## 54                      1189.53488                    1090.11628
## 55                      1189.53488                    1090.11628
## 56                      1189.53488                    1090.11628
## 57                      1189.53488                    1090.11628
## 58                      1189.53488                    1090.11628
## 59                      1189.53488                    1090.11628
## 60                      1189.53488                    1090.11628
## 61                      1189.53488                    1090.11628
## 62                      1189.53488                    1090.11628
## 63                      1189.53488                    1090.11628
## 64                      1189.53488                    1090.11628
## 65                      1189.53488                    1090.11628
## 66                      1189.53488                    1090.11628
## 67                      1189.53488                    1090.11628
## 68                      1189.53488                    1090.11628
## 69                      1189.53488                    1090.11628
## 70                      1189.53488                    1090.11628
## 71                      1189.53488                    1090.11628
## 72                      1189.53488                    1090.11628
## 73                      3783.13953                    2180.23256
## 74                      3783.13953                    2180.23256
## 75                      3783.13953                    2180.23256
## 76                      3783.13953                    2180.23256
## 77                      3783.13953                    2180.23256
## 78                      3783.13953                    2180.23256
## 79                      3783.13953                    2180.23256
## 80                      3783.13953                    2180.23256
## 81                      3783.13953                    2180.23256
## 82                      3783.13953                    2180.23256
## 83                      3783.13953                    2180.23256
## 84                      3783.13953                    2180.23256
## 85                      3783.13953                    2180.23256
## 86                      3783.13953                    2180.23256
## 87                      3783.13953                    2180.23256
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## 2777                    2912.79070                    3427.32558
## 2778                    2912.79070                    3427.32558
## 2779                    2912.79070                    3427.32558
## 2780                    2912.79070                    3427.32558
## 2781                    2912.79070                    3427.32558
## 2782                    2912.79070                    3427.32558
## 2783                    2912.79070                    3427.32558
## 2784                    2912.79070                    3427.32558
## 2785                     409.88372                    3706.39535
## 2786                     409.88372                    3706.39535
## 2787                     409.88372                    3706.39535
## 2788                     409.88372                    3706.39535
## 2789                     409.88372                    3706.39535
## 2790                     409.88372                    3706.39535
## 2791                     409.88372                    3706.39535
## 2792                     409.88372                    3706.39535
## 2793                     409.88372                    3706.39535
## 2794                     409.88372                    3706.39535
## 2795                     409.88372                    3706.39535
## 2796                     409.88372                    3706.39535
## 2797                     409.88372                    3706.39535
## 2798                     409.88372                    3706.39535
## 2799                     409.88372                    3706.39535
## 2800                     409.88372                    3706.39535
## 2801                     409.88372                    3706.39535
## 2802                     409.88372                    3706.39535
## 2803                     409.88372                    3706.39535
## 2804                     409.88372                    3706.39535
## 2805                     409.88372                    3706.39535
## 2806                     409.88372                    3706.39535
## 2807                     409.88372                    3706.39535
## 2808                     409.88372                    3706.39535
## 2809                    2406.97674                    1090.11628
## 2810                    2406.97674                    1090.11628
## 2811                    2406.97674                    1090.11628
## 2812                    2406.97674                    1090.11628
## 2813                    2406.97674                    1090.11628
## 2814                    2406.97674                    1090.11628
## 2815                    2406.97674                    1090.11628
## 2816                    2406.97674                    1090.11628
## 2817                    2406.97674                    1090.11628
## 2818                    2406.97674                    1090.11628
## 2819                    2406.97674                    1090.11628
## 2820                    2406.97674                    1090.11628
## 2821                    2406.97674                    1090.11628
## 2822                    2406.97674                    1090.11628
## 2823                    2406.97674                    1090.11628
## 2824                    2406.97674                    1090.11628
## 2825                    2406.97674                    1090.11628
## 2826                    2406.97674                    1090.11628
## 2827                    2406.97674                    1090.11628
## 2828                    2406.97674                    1090.11628
## 2829                    2406.97674                    1090.11628
## 2830                    2406.97674                    1090.11628
## 2831                    2406.97674                    1090.11628
## 2832                    2406.97674                    1090.11628
## 2833                    2214.24419                    1090.11628
## 2834                    2214.24419                    1090.11628
## 2835                    2214.24419                    1090.11628
## 2836                    2214.24419                    1090.11628
## 2837                    2214.24419                    1090.11628
## 2838                    2214.24419                    1090.11628
## 2839                    2214.24419                    1090.11628
## 2840                    2214.24419                    1090.11628
## 2841                    2214.24419                    1090.11628
## 2842                    2214.24419                    1090.11628
## 2843                    2214.24419                    1090.11628
## 2844                    2214.24419                    1090.11628
## 2845                    2214.24419                    1090.11628
## 2846                    2214.24419                    1090.11628
## 2847                    2214.24419                    1090.11628
## 2848                    2214.24419                    1090.11628
## 2849                    2214.24419                    1090.11628
## 2850                    2214.24419                    1090.11628
## 2851                    2214.24419                    1090.11628
## 2852                    2214.24419                    1090.11628
## 2853                    2214.24419                    1090.11628
## 2854                    2214.24419                    1090.11628
## 2855                    2214.24419                    1090.11628
## 2856                    2214.24419                    1090.11628
## 2857                    1848.83721                       0.00000
## 2858                    1848.83721                       0.00000
## 2859                    1848.83721                       0.00000
## 2860                    1848.83721                       0.00000
## 2861                    1848.83721                       0.00000
## 2862                    1848.83721                       0.00000
## 2863                    1848.83721                       0.00000
## 2864                    1848.83721                       0.00000
## 2865                    1848.83721                       0.00000
## 2866                    1848.83721                       0.00000
## 2867                    1848.83721                       0.00000
## 2868                    1848.83721                       0.00000
## 2869                    1848.83721                       0.00000
## 2870                    1848.83721                       0.00000
## 2871                    1848.83721                       0.00000
## 2872                    1848.83721                       0.00000
## 2873                    1848.83721                       0.00000
## 2874                    1848.83721                       0.00000
## 2875                    1848.83721                       0.00000
## 2876                    1848.83721                       0.00000
## 2877                    1848.83721                       0.00000
## 2878                    1848.83721                       0.00000
## 2879                    1848.83721                       0.00000
## 2880                    1848.83721                       0.00000
## 2881                     883.43023                     635.75581
## 2882                     883.43023                     635.75581
## 2883                     883.43023                     635.75581
## 2884                     883.43023                     635.75581
## 2885                     883.43023                     635.75581
## 2886                     883.43023                     635.75581
## 2887                     883.43023                     635.75581
## 2888                     883.43023                     635.75581
## 2889                     883.43023                     635.75581
## 2890                     883.43023                     635.75581
## 2891                     883.43023                     635.75581
## 2892                     883.43023                     635.75581
## 2893                     883.43023                     635.75581
## 2894                     883.43023                     635.75581
## 2895                     883.43023                     635.75581
## 2896                     883.43023                     635.75581
## 2897                     883.43023                     635.75581
## 2898                     883.43023                     635.75581
## 2899                     883.43023                     635.75581
## 2900                     883.43023                     635.75581
## 2901                     883.43023                     635.75581
## 2902                     883.43023                     635.75581
## 2903                     883.43023                     635.75581
## 2904                     883.43023                     635.75581
## 2905                    2150.58140                    1055.23256
## 2906                    2150.58140                    1055.23256
## 2907                    2150.58140                    1055.23256
## 2908                    2150.58140                    1055.23256
## 2909                    2150.58140                    1055.23256
## 2910                    2150.58140                    1055.23256
## 2911                    2150.58140                    1055.23256
## 2912                    2150.58140                    1055.23256
## 2913                    2150.58140                    1055.23256
## 2914                    2150.58140                    1055.23256
## 2915                    2150.58140                    1055.23256
## 2916                    2150.58140                    1055.23256
## 2917                    2150.58140                    1055.23256
## 2918                    2150.58140                    1055.23256
## 2919                    2150.58140                    1055.23256
## 2920                    2150.58140                    1055.23256
## 2921                    2150.58140                    1055.23256
## 2922                    2150.58140                    1055.23256
## 2923                    2150.58140                    1055.23256
## 2924                    2150.58140                    1055.23256
## 2925                    2150.58140                    1055.23256
## 2926                    2150.58140                    1055.23256
## 2927                    2150.58140                    1055.23256
## 2928                    2150.58140                    1055.23256
## 2929                    1090.11628                    1090.11628
## 2930                    1090.11628                    1090.11628
## 2931                    1090.11628                    1090.11628
## 2932                    1090.11628                    1090.11628
## 2933                    1090.11628                    1090.11628
## 2934                    1090.11628                    1090.11628
## 2935                    1090.11628                    1090.11628
## 2936                    1090.11628                    1090.11628
## 2937                    1090.11628                    1090.11628
## 2938                    1090.11628                    1090.11628
## 2939                    1090.11628                    1090.11628
## 2940                    1090.11628                    1090.11628
## 2941                    1090.11628                    1090.11628
## 2942                    1090.11628                    1090.11628
## 2943                    1090.11628                    1090.11628
## 2944                    1090.11628                    1090.11628
## 2945                    1090.11628                    1090.11628
## 2946                    1090.11628                    1090.11628
## 2947                    1090.11628                    1090.11628
## 2948                    1090.11628                    1090.11628
## 2949                    1090.11628                    1090.11628
## 2950                    1090.11628                    1090.11628
## 2951                    1090.11628                    1090.11628
## 2952                    1090.11628                    1090.11628
## 2953                    3636.62791                    2180.23256
## 2954                    3636.62791                    2180.23256
## 2955                    3636.62791                    2180.23256
## 2956                    3636.62791                    2180.23256
## 2957                    3636.62791                    2180.23256
## 2958                    3636.62791                    2180.23256
## 2959                    3636.62791                    2180.23256
## 2960                    3636.62791                    2180.23256
## 2961                    3636.62791                    2180.23256
## 2962                    3636.62791                    2180.23256
## 2963                    3636.62791                    2180.23256
## 2964                    3636.62791                    2180.23256
## 2965                    3636.62791                    2180.23256
## 2966                    3636.62791                    2180.23256
## 2967                    3636.62791                    2180.23256
## 2968                    3636.62791                    2180.23256
## 2969                    3636.62791                    2180.23256
## 2970                    3636.62791                    2180.23256
## 2971                    3636.62791                    2180.23256
## 2972                    3636.62791                    2180.23256
## 2973                    3636.62791                    2180.23256
## 2974                    3636.62791                    2180.23256
## 2975                    3636.62791                    2180.23256
## 2976                    3636.62791                    2180.23256
## 2977                    1709.30233                     549.41860
## 2978                    1709.30233                     549.41860
## 2979                    1709.30233                     549.41860
## 2980                    1709.30233                     549.41860
## 2981                    1709.30233                     549.41860
## 2982                    1709.30233                     549.41860
## 2983                    1709.30233                     549.41860
## 2984                    1709.30233                     549.41860
## 2985                    1709.30233                     549.41860
## 2986                    1709.30233                     549.41860
## 2987                    1709.30233                     549.41860
## 2988                    1709.30233                     549.41860
## 2989                    1709.30233                     549.41860
## 2990                    1709.30233                     549.41860
## 2991                    1709.30233                     549.41860
## 2992                    1709.30233                     549.41860
## 2993                    1709.30233                     549.41860
## 2994                    1709.30233                     549.41860
## 2995                    1709.30233                     549.41860
## 2996                    1709.30233                     549.41860
## 2997                    1709.30233                     549.41860
## 2998                    1709.30233                     549.41860
## 2999                    1709.30233                     549.41860
## 3000                    1709.30233                     549.41860
## 3001                     594.76744                       0.00000
## 3002                     594.76744                       0.00000
## 3003                     594.76744                       0.00000
## 3004                     594.76744                       0.00000
## 3005                     594.76744                       0.00000
## 3006                     594.76744                       0.00000
## 3007                     594.76744                       0.00000
## 3008                     594.76744                       0.00000
## 3009                     594.76744                       0.00000
## 3010                     594.76744                       0.00000
## 3011                     594.76744                       0.00000
## 3012                     594.76744                       0.00000
## 3013                     594.76744                       0.00000
## 3014                     594.76744                       0.00000
## 3015                     594.76744                       0.00000
## 3016                     594.76744                       0.00000
## 3017                     594.76744                       0.00000
## 3018                     594.76744                       0.00000
## 3019                     594.76744                       0.00000
## 3020                     594.76744                       0.00000
## 3021                     594.76744                       0.00000
## 3022                     594.76744                       0.00000
## 3023                     594.76744                       0.00000
## 3024                     594.76744                       0.00000
## 3025                    1181.68605                       0.00000
## 3026                    1181.68605                       0.00000
## 3027                    1181.68605                       0.00000
## 3028                    1181.68605                       0.00000
## 3029                    1181.68605                       0.00000
## 3030                    1181.68605                       0.00000
## 3031                    1181.68605                       0.00000
## 3032                    1181.68605                       0.00000
## 3033                    1181.68605                       0.00000
## 3034                    1181.68605                       0.00000
## 3035                    1181.68605                       0.00000
## 3036                    1181.68605                       0.00000
## 3037                    1181.68605                       0.00000
## 3038                    1181.68605                       0.00000
## 3039                    1181.68605                       0.00000
## 3040                    1181.68605                       0.00000
## 3041                    1181.68605                       0.00000
## 3042                    1181.68605                       0.00000
## 3043                    1181.68605                       0.00000
## 3044                    1181.68605                       0.00000
## 3045                    1181.68605                       0.00000
## 3046                    1181.68605                       0.00000
## 3047                    1181.68605                       0.00000
## 3048                    1181.68605                       0.00000
## 3049                    1172.96512                     126.45349
## 3050                    1172.96512                     126.45349
## 3051                    1172.96512                     126.45349
## 3052                    1172.96512                     126.45349
## 3053                    1172.96512                     126.45349
## 3054                    1172.96512                     126.45349
## 3055                    1172.96512                     126.45349
## 3056                    1172.96512                     126.45349
## 3057                    1172.96512                     126.45349
## 3058                    1172.96512                     126.45349
## 3059                    1172.96512                     126.45349
## 3060                    1172.96512                     126.45349
## 3061                    1172.96512                     126.45349
## 3062                    1172.96512                     126.45349
## 3063                    1172.96512                     126.45349
## 3064                    1172.96512                     126.45349
## 3065                    1172.96512                     126.45349
## 3066                    1172.96512                     126.45349
## 3067                    1172.96512                     126.45349
## 3068                    1172.96512                     126.45349
## 3069                    1172.96512                     126.45349
## 3070                    1172.96512                     126.45349
## 3071                    1172.96512                     126.45349
## 3072                    1172.96512                     126.45349
## 3073                     883.43023                     635.75581
## 3074                     883.43023                     635.75581
## 3075                     883.43023                     635.75581
## 3076                     883.43023                     635.75581
## 3077                     883.43023                     635.75581
## 3078                     883.43023                     635.75581
## 3079                     883.43023                     635.75581
## 3080                     883.43023                     635.75581
## 3081                     883.43023                     635.75581
## 3082                     883.43023                     635.75581
## 3083                     883.43023                     635.75581
## 3084                     883.43023                     635.75581
## 3085                     883.43023                     635.75581
## 3086                     883.43023                     635.75581
## 3087                     883.43023                     635.75581
## 3088                     883.43023                     635.75581
## 3089                     883.43023                     635.75581
## 3090                     883.43023                     635.75581
## 3091                     883.43023                     635.75581
## 3092                     883.43023                     635.75581
## 3093                     883.43023                     635.75581
## 3094                     883.43023                     635.75581
## 3095                     883.43023                     635.75581
## 3096                     883.43023                     635.75581
## 3097                    6005.23256                    1029.06977
## 3098                    6005.23256                    1029.06977
## 3099                    6005.23256                    1029.06977
## 3100                    6005.23256                    1029.06977
## 3101                    6005.23256                    1029.06977
## 3102                    6005.23256                    1029.06977
## 3103                    6005.23256                    1029.06977
## 3104                    6005.23256                    1029.06977
## 3105                    6005.23256                    1029.06977
## 3106                    6005.23256                    1029.06977
## 3107                    6005.23256                    1029.06977
## 3108                    6005.23256                    1029.06977
## 3109                    6005.23256                    1029.06977
## 3110                    6005.23256                    1029.06977
## 3111                    6005.23256                    1029.06977
## 3112                    6005.23256                    1029.06977
## 3113                    6005.23256                    1029.06977
## 3114                    6005.23256                    1029.06977
## 3115                    6005.23256                    1029.06977
## 3116                    6005.23256                    1029.06977
## 3117                    6005.23256                    1029.06977
## 3118                    6005.23256                    1029.06977
## 3119                    6005.23256                    1029.06977
## 3120                    6005.23256                    1029.06977
## 3121                       0.00000                       0.00000
## 3122                       0.00000                       0.00000
## 3123                       0.00000                       0.00000
## 3124                       0.00000                       0.00000
## 3125                       0.00000                       0.00000
## 3126                       0.00000                       0.00000
## 3127                       0.00000                       0.00000
## 3128                       0.00000                       0.00000
## 3129                       0.00000                       0.00000
## 3130                       0.00000                       0.00000
## 3131                       0.00000                       0.00000
## 3132                       0.00000                       0.00000
## 3133                       0.00000                       0.00000
## 3134                       0.00000                       0.00000
## 3135                       0.00000                       0.00000
## 3136                       0.00000                       0.00000
## 3137                       0.00000                       0.00000
## 3138                       0.00000                       0.00000
## 3139                       0.00000                       0.00000
## 3140                       0.00000                       0.00000
## 3141                       0.00000                       0.00000
## 3142                       0.00000                       0.00000
## 3143                       0.00000                       0.00000
## 3144                       0.00000                       0.00000
## 3145                       0.00000                    2188.95349
## 3146                       0.00000                    2188.95349
## 3147                       0.00000                    2188.95349
## 3148                       0.00000                    2188.95349
## 3149                       0.00000                    2188.95349
## 3150                       0.00000                    2188.95349
## 3151                       0.00000                    2188.95349
## 3152                       0.00000                    2188.95349
## 3153                       0.00000                    2188.95349
## 3154                       0.00000                    2188.95349
## 3155                       0.00000                    2188.95349
## 3156                       0.00000                    2188.95349
## 3157                       0.00000                    2188.95349
## 3158                       0.00000                    2188.95349
## 3159                       0.00000                    2188.95349
## 3160                       0.00000                    2188.95349
## 3161                       0.00000                    2188.95349
## 3162                       0.00000                    2188.95349
## 3163                       0.00000                    2188.95349
## 3164                       0.00000                    2188.95349
## 3165                       0.00000                    2188.95349
## 3166                       0.00000                    2188.95349
## 3167                       0.00000                    2188.95349
## 3168                       0.00000                    2188.95349
## 3169                       0.00000                    3654.06977
## 3170                       0.00000                    3654.06977
## 3171                       0.00000                    3654.06977
## 3172                       0.00000                    3654.06977
## 3173                       0.00000                    3654.06977
## 3174                       0.00000                    3654.06977
## 3175                       0.00000                    3654.06977
## 3176                       0.00000                    3654.06977
## 3177                       0.00000                    3654.06977
## 3178                       0.00000                    3654.06977
## 3179                       0.00000                    3654.06977
## 3180                       0.00000                    3654.06977
## 3181                       0.00000                    3654.06977
## 3182                       0.00000                    3654.06977
## 3183                       0.00000                    3654.06977
## 3184                       0.00000                    3654.06977
## 3185                       0.00000                    3654.06977
## 3186                       0.00000                    3654.06977
## 3187                       0.00000                    3654.06977
## 3188                       0.00000                    3654.06977
## 3189                       0.00000                    3654.06977
## 3190                       0.00000                    3654.06977
## 3191                       0.00000                    3654.06977
## 3192                       0.00000                    3654.06977
## 3193                    1030.81395                     287.79070
## 3194                    1030.81395                     287.79070
## 3195                    1030.81395                     287.79070
## 3196                    1030.81395                     287.79070
## 3197                    1030.81395                     287.79070
## 3198                    1030.81395                     287.79070
## 3199                    1030.81395                     287.79070
## 3200                    1030.81395                     287.79070
## 3201                    1030.81395                     287.79070
## 3202                    1030.81395                     287.79070
## 3203                    1030.81395                     287.79070
## 3204                    1030.81395                     287.79070
## 3205                    1030.81395                     287.79070
## 3206                    1030.81395                     287.79070
## 3207                    1030.81395                     287.79070
## 3208                    1030.81395                     287.79070
## 3209                    1030.81395                     287.79070
## 3210                    1030.81395                     287.79070
## 3211                    1030.81395                     287.79070
## 3212                    1030.81395                     287.79070
## 3213                    1030.81395                     287.79070
## 3214                    1030.81395                     287.79070
## 3215                    1030.81395                     287.79070
## 3216                    1030.81395                     287.79070
## 3217                    1360.46512                       0.00000
## 3218                    1360.46512                       0.00000
## 3219                    1360.46512                       0.00000
## 3220                    1360.46512                       0.00000
## 3221                    1360.46512                       0.00000
## 3222                    1360.46512                       0.00000
## 3223                    1360.46512                       0.00000
## 3224                    1360.46512                       0.00000
## 3225                    1360.46512                       0.00000
##      total_metaecosystem_Spi_te_indiv total_metaecosystem_Tet_indiv connection
## 1                            981.9767                      2243.895   isolated
## 2                            981.9767                      2243.895   isolated
## 3                            981.9767                      2243.895   isolated
## 4                            981.9767                      2243.895   isolated
## 5                            981.9767                      2243.895   isolated
## 6                            981.9767                      2243.895   isolated
## 7                            981.9767                      2243.895   isolated
## 8                            981.9767                      2243.895   isolated
## 9                            981.9767                      2243.895   isolated
## 10                           981.9767                      2243.895   isolated
## 11                           981.9767                      2243.895   isolated
## 12                           981.9767                      2243.895   isolated
## 13                           981.9767                      2243.895   isolated
## 14                           981.9767                      2243.895   isolated
## 15                           981.9767                      2243.895   isolated
## 16                           981.9767                      2243.895   isolated
## 17                           981.9767                      2243.895   isolated
## 18                           981.9767                      2243.895   isolated
## 19                           981.9767                      2243.895   isolated
## 20                           981.9767                      2243.895   isolated
## 21                           981.9767                      2243.895   isolated
## 22                           981.9767                      2243.895   isolated
## 23                           981.9767                      2243.895   isolated
## 24                           981.9767                      2243.895   isolated
## 25                          1090.1163                         0.000   isolated
## 26                          1090.1163                         0.000   isolated
## 27                          1090.1163                         0.000   isolated
## 28                          1090.1163                         0.000   isolated
## 29                          1090.1163                         0.000   isolated
## 30                          1090.1163                         0.000   isolated
## 31                          1090.1163                         0.000   isolated
## 32                          1090.1163                         0.000   isolated
## 33                          1090.1163                         0.000   isolated
## 34                          1090.1163                         0.000   isolated
## 35                          1090.1163                         0.000   isolated
## 36                          1090.1163                         0.000   isolated
## 37                          1090.1163                         0.000   isolated
## 38                          1090.1163                         0.000   isolated
## 39                          1090.1163                         0.000   isolated
## 40                          1090.1163                         0.000   isolated
## 41                          1090.1163                         0.000   isolated
## 42                          1090.1163                         0.000   isolated
## 43                          1090.1163                         0.000   isolated
## 44                          1090.1163                         0.000   isolated
## 45                          1090.1163                         0.000   isolated
## 46                          1090.1163                         0.000   isolated
## 47                          1090.1163                         0.000   isolated
## 48                          1090.1163                         0.000   isolated
## 49                          2084.3023                         0.000   isolated
## 50                          2084.3023                         0.000   isolated
## 51                          2084.3023                         0.000   isolated
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##      patch_size_symmetry set ID_first_patch ID_second_patch
## 1             asymmetric   1              1              11
## 2             asymmetric   2              1              11
## 3             asymmetric   3              1              11
## 4             asymmetric   4              1              11
## 5             asymmetric   7              1              11
## 6             asymmetric   8              1              11
## 7             asymmetric   9              1              11
## 8             asymmetric  10              1              11
## 9             asymmetric  11              1              11
## 10            asymmetric  12              1              11
## 11            asymmetric  13              1              11
## 12            asymmetric  14              1              11
## 13            asymmetric  27              1              11
## 14            asymmetric  28              1              11
## 15            asymmetric  29              1              11
## 16            asymmetric  30              1              11
## 17            asymmetric  31              1              11
## 18            asymmetric  32              1              11
## 19            asymmetric  33              1              11
## 20            asymmetric  34              1              11
## 21            asymmetric  37              1              11
## 22            asymmetric  38              1              11
## 23            asymmetric  39              1              11
## 24            asymmetric  40              1              11
## 25            asymmetric   1              1              11
## 26            asymmetric   2              1              11
## 27            asymmetric   3              1              11
## 28            asymmetric   4              1              11
## 29            asymmetric   7              1              11
## 30            asymmetric   8              1              11
## 31            asymmetric   9              1              11
## 32            asymmetric  10              1              11
## 33            asymmetric  11              1              11
## 34            asymmetric  12              1              11
## 35            asymmetric  13              1              11
## 36            asymmetric  14              1              11
## 37            asymmetric  27              1              11
## 38            asymmetric  28              1              11
## 39            asymmetric  29              1              11
## 40            asymmetric  30              1              11
## 41            asymmetric  31              1              11
## 42            asymmetric  32              1              11
## 43            asymmetric  33              1              11
## 44            asymmetric  34              1              11
## 45            asymmetric  37              1              11
## 46            asymmetric  38              1              11
## 47            asymmetric  39              1              11
## 48            asymmetric  40              1              11
## 49            asymmetric   1              1              11
## 50            asymmetric   2              1              11
## 51            asymmetric   3              1              11
## 52            asymmetric   4              1              11
## 53            asymmetric   7              1              11
## 54            asymmetric   8              1              11
## 55            asymmetric   9              1              11
## 56            asymmetric  10              1              11
## 57            asymmetric  11              1              11
## 58            asymmetric  12              1              11
## 59            asymmetric  13              1              11
## 60            asymmetric  14              1              11
## 61            asymmetric  27              1              11
## 62            asymmetric  28              1              11
## 63            asymmetric  29              1              11
## 64            asymmetric  30              1              11
## 65            asymmetric  31              1              11
## 66            asymmetric  32              1              11
## 67            asymmetric  33              1              11
## 68            asymmetric  34              1              11
## 69            asymmetric  37              1              11
## 70            asymmetric  38              1              11
## 71            asymmetric  39              1              11
## 72            asymmetric  40              1              11
## 73            asymmetric   1              1              11
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## 75            asymmetric   3              1              11
## 76            asymmetric   4              1              11
## 77            asymmetric   7              1              11
## 78            asymmetric   8              1              11
## 79            asymmetric   9              1              11
## 80            asymmetric  10              1              11
## 81            asymmetric  11              1              11
## 82            asymmetric  12              1              11
## 83            asymmetric  13              1              11
## 84            asymmetric  14              1              11
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## 86            asymmetric  28              1              11
## 87            asymmetric  29              1              11
## 88            asymmetric  30              1              11
## 89            asymmetric  31              1              11
## 90            asymmetric  32              1              11
## 91            asymmetric  33              1              11
## 92            asymmetric  34              1              11
## 93            asymmetric  37              1              11
## 94            asymmetric  38              1              11
## 95            asymmetric  39              1              11
## 96            asymmetric  40              1              11
## 97            asymmetric   1              1              11
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## 99            asymmetric   3              1              11
## 100           asymmetric   4              1              11
## 101           asymmetric   7              1              11
## 102           asymmetric   8              1              11
## 103           asymmetric   9              1              11
## 104           asymmetric  10              1              11
## 105           asymmetric  11              1              11
## 106           asymmetric  12              1              11
## 107           asymmetric  13              1              11
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## 110           asymmetric  28              1              11
## 111           asymmetric  29              1              11
## 112           asymmetric  30              1              11
## 113           asymmetric  31              1              11
## 114           asymmetric  32              1              11
## 115           asymmetric  33              1              11
## 116           asymmetric  34              1              11
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## 118           asymmetric  38              1              11
## 119           asymmetric  39              1              11
## 120           asymmetric  40              1              11
## 121           asymmetric   1              1              11
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## 126           asymmetric   8              1              11
## 127           asymmetric   9              1              11
## 128           asymmetric  10              1              11
## 129           asymmetric  11              1              11
## 130           asymmetric  12              1              11
## 131           asymmetric  13              1              11
## 132           asymmetric  14              1              11
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## 134           asymmetric  28              1              11
## 135           asymmetric  29              1              11
## 136           asymmetric  30              1              11
## 137           asymmetric  31              1              11
## 138           asymmetric  32              1              11
## 139           asymmetric  33              1              11
## 140           asymmetric  34              1              11
## 141           asymmetric  37              1              11
## 142           asymmetric  38              1              11
## 143           asymmetric  39              1              11
## 144           asymmetric  40              1              11
## 145           asymmetric   1              1              11
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## 149           asymmetric   7              1              11
## 150           asymmetric   8              1              11
## 151           asymmetric   9              1              11
## 152           asymmetric  10              1              11
## 153           asymmetric  11              1              11
## 154           asymmetric  12              1              11
## 155           asymmetric  13              1              11
## 156           asymmetric  14              1              11
## 157           asymmetric  27              1              11
## 158           asymmetric  28              1              11
## 159           asymmetric  29              1              11
## 160           asymmetric  30              1              11
## 161           asymmetric  31              1              11
## 162           asymmetric  32              1              11
## 163           asymmetric  33              1              11
## 164           asymmetric  34              1              11
## 165           asymmetric  37              1              11
## 166           asymmetric  38              1              11
## 167           asymmetric  39              1              11
## 168           asymmetric  40              1              11
## 169           asymmetric   1              1              11
## 170           asymmetric   2              1              11
## 171           asymmetric   3              1              11
## 172           asymmetric   4              1              11
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## 174           asymmetric   8              1              11
## 175           asymmetric   9              1              11
## 176           asymmetric  10              1              11
## 177           asymmetric  11              1              11
## 178           asymmetric  12              1              11
## 179           asymmetric  13              1              11
## 180           asymmetric  14              1              11
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## 182           asymmetric  28              1              11
## 183           asymmetric  29              1              11
## 184           asymmetric  30              1              11
## 185           asymmetric  31              1              11
## 186           asymmetric  32              1              11
## 187           asymmetric  33              1              11
## 188           asymmetric  34              1              11
## 189           asymmetric  37              1              11
## 190           asymmetric  38              1              11
## 191           asymmetric  39              1              11
## 192           asymmetric  40              1              11
## 193           asymmetric  81              1              12
## 194           asymmetric  82              1              12
## 195           asymmetric  83              1              12
## 196           asymmetric  84              1              12
## 197           asymmetric  87              1              12
## 198           asymmetric  88              1              12
## 199           asymmetric  89              1              12
## 200           asymmetric  90              1              12
## 201           asymmetric  91              1              12
## 202           asymmetric  92              1              12
## 203           asymmetric  93              1              12
## 204           asymmetric  94              1              12
## 205           asymmetric 107              1              12
## 206           asymmetric 108              1              12
## 207           asymmetric 109              1              12
## 208           asymmetric 110              1              12
## 209           asymmetric 111              1              12
## 210           asymmetric 112              1              12
## 211           asymmetric 113              1              12
## 212           asymmetric 114              1              12
## 213           asymmetric 117              1              12
## 214           asymmetric 118              1              12
## 215           asymmetric 119              1              12
## 216           asymmetric 120              1              12
## 217           asymmetric  81              1              12
## 218           asymmetric  82              1              12
## 219           asymmetric  83              1              12
## 220           asymmetric  84              1              12
## 221           asymmetric  87              1              12
## 222           asymmetric  88              1              12
## 223           asymmetric  89              1              12
## 224           asymmetric  90              1              12
## 225           asymmetric  91              1              12
## 226           asymmetric  92              1              12
## 227           asymmetric  93              1              12
## 228           asymmetric  94              1              12
## 229           asymmetric 107              1              12
## 230           asymmetric 108              1              12
## 231           asymmetric 109              1              12
## 232           asymmetric 110              1              12
## 233           asymmetric 111              1              12
## 234           asymmetric 112              1              12
## 235           asymmetric 113              1              12
## 236           asymmetric 114              1              12
## 237           asymmetric 117              1              12
## 238           asymmetric 118              1              12
## 239           asymmetric 119              1              12
## 240           asymmetric 120              1              12
## 241           asymmetric  81              1              12
## 242           asymmetric  82              1              12
## 243           asymmetric  83              1              12
## 244           asymmetric  84              1              12
## 245           asymmetric  87              1              12
## 246           asymmetric  88              1              12
## 247           asymmetric  89              1              12
## 248           asymmetric  90              1              12
## 249           asymmetric  91              1              12
## 250           asymmetric  92              1              12
## 251           asymmetric  93              1              12
## 252           asymmetric  94              1              12
## 253           asymmetric 107              1              12
## 254           asymmetric 108              1              12
## 255           asymmetric 109              1              12
## 256           asymmetric 110              1              12
## 257           asymmetric 111              1              12
## 258           asymmetric 112              1              12
## 259           asymmetric 113              1              12
## 260           asymmetric 114              1              12
## 261           asymmetric 117              1              12
## 262           asymmetric 118              1              12
## 263           asymmetric 119              1              12
## 264           asymmetric 120              1              12
## 265           asymmetric  81              1              12
## 266           asymmetric  82              1              12
## 267           asymmetric  83              1              12
## 268           asymmetric  84              1              12
## 269           asymmetric  87              1              12
## 270           asymmetric  88              1              12
## 271           asymmetric  89              1              12
## 272           asymmetric  90              1              12
## 273           asymmetric  91              1              12
## 274           asymmetric  92              1              12
## 275           asymmetric  93              1              12
## 276           asymmetric  94              1              12
## 277           asymmetric 107              1              12
## 278           asymmetric 108              1              12
## 279           asymmetric 109              1              12
## 280           asymmetric 110              1              12
## 281           asymmetric 111              1              12
## 282           asymmetric 112              1              12
## 283           asymmetric 113              1              12
## 284           asymmetric 114              1              12
## 285           asymmetric 117              1              12
## 286           asymmetric 118              1              12
## 287           asymmetric 119              1              12
## 288           asymmetric 120              1              12
## 289           asymmetric  81              1              12
## 290           asymmetric  82              1              12
## 291           asymmetric  83              1              12
## 292           asymmetric  84              1              12
## 293           asymmetric  87              1              12
## 294           asymmetric  88              1              12
## 295           asymmetric  89              1              12
## 296           asymmetric  90              1              12
## 297           asymmetric  91              1              12
## 298           asymmetric  92              1              12
## 299           asymmetric  93              1              12
## 300           asymmetric  94              1              12
## 301           asymmetric 107              1              12
## 302           asymmetric 108              1              12
## 303           asymmetric 109              1              12
## 304           asymmetric 110              1              12
## 305           asymmetric 111              1              12
## 306           asymmetric 112              1              12
## 307           asymmetric 113              1              12
## 308           asymmetric 114              1              12
## 309           asymmetric 117              1              12
## 310           asymmetric 118              1              12
## 311           asymmetric 119              1              12
## 312           asymmetric 120              1              12
## 313           asymmetric  81              1              12
## 314           asymmetric  82              1              12
## 315           asymmetric  83              1              12
## 316           asymmetric  84              1              12
## 317           asymmetric  87              1              12
## 318           asymmetric  88              1              12
## 319           asymmetric  89              1              12
## 320           asymmetric  90              1              12
## 321           asymmetric  91              1              12
## 322           asymmetric  92              1              12
## 323           asymmetric  93              1              12
## 324           asymmetric  94              1              12
## 325           asymmetric 107              1              12
## 326           asymmetric 108              1              12
## 327           asymmetric 109              1              12
## 328           asymmetric 110              1              12
## 329           asymmetric 111              1              12
## 330           asymmetric 112              1              12
## 331           asymmetric 113              1              12
## 332           asymmetric 114              1              12
## 333           asymmetric 117              1              12
## 334           asymmetric 118              1              12
## 335           asymmetric 119              1              12
## 336           asymmetric 120              1              12
## 337           asymmetric  81              1              12
## 338           asymmetric  82              1              12
## 339           asymmetric  83              1              12
## 340           asymmetric  84              1              12
## 341           asymmetric  87              1              12
## 342           asymmetric  88              1              12
## 343           asymmetric  89              1              12
## 344           asymmetric  90              1              12
## 345           asymmetric  91              1              12
## 346           asymmetric  92              1              12
## 347           asymmetric  93              1              12
## 348           asymmetric  94              1              12
## 349           asymmetric 107              1              12
## 350           asymmetric 108              1              12
## 351           asymmetric 109              1              12
## 352           asymmetric 110              1              12
## 353           asymmetric 111              1              12
## 354           asymmetric 112              1              12
## 355           asymmetric 113              1              12
## 356           asymmetric 114              1              12
## 357           asymmetric 117              1              12
## 358           asymmetric 118              1              12
## 359           asymmetric 119              1              12
## 360           asymmetric 120              1              12
## 361           asymmetric  81              1              12
## 362           asymmetric  82              1              12
## 363           asymmetric  83              1              12
## 364           asymmetric  84              1              12
## 365           asymmetric  87              1              12
## 366           asymmetric  88              1              12
## 367           asymmetric  89              1              12
## 368           asymmetric  90              1              12
## 369           asymmetric  91              1              12
## 370           asymmetric  92              1              12
## 371           asymmetric  93              1              12
## 372           asymmetric  94              1              12
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## 374           asymmetric 108              1              12
## 375           asymmetric 109              1              12
## 376           asymmetric 110              1              12
## 377           asymmetric 111              1              12
## 378           asymmetric 112              1              12
## 379           asymmetric 113              1              12
## 380           asymmetric 114              1              12
## 381           asymmetric 117              1              12
## 382           asymmetric 118              1              12
## 383           asymmetric 119              1              12
## 384           asymmetric 120              1              12
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## 386           asymmetric  42              1              13
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## 388           asymmetric  44              1              13
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## 390           asymmetric  48              1              13
## 391           asymmetric  49              1              13
## 392           asymmetric  50              1              13
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## 394           asymmetric  52              1              13
## 395           asymmetric  53              1              13
## 396           asymmetric  54              1              13
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## 398           asymmetric  68              1              13
## 399           asymmetric  69              1              13
## 400           asymmetric  70              1              13
## 401           asymmetric  71              1              13
## 402           asymmetric  72              1              13
## 403           asymmetric  73              1              13
## 404           asymmetric  74              1              13
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## 406           asymmetric  78              1              13
## 407           asymmetric  79              1              13
## 408           asymmetric  80              1              13
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## 410           asymmetric  42              1              13
## 411           asymmetric  43              1              13
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## 414           asymmetric  48              1              13
## 415           asymmetric  49              1              13
## 416           asymmetric  50              1              13
## 417           asymmetric  51              1              13
## 418           asymmetric  52              1              13
## 419           asymmetric  53              1              13
## 420           asymmetric  54              1              13
## 421           asymmetric  67              1              13
## 422           asymmetric  68              1              13
## 423           asymmetric  69              1              13
## 424           asymmetric  70              1              13
## 425           asymmetric  71              1              13
## 426           asymmetric  72              1              13
## 427           asymmetric  73              1              13
## 428           asymmetric  74              1              13
## 429           asymmetric  77              1              13
## 430           asymmetric  78              1              13
## 431           asymmetric  79              1              13
## 432           asymmetric  80              1              13
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## 434           asymmetric  42              1              13
## 435           asymmetric  43              1              13
## 436           asymmetric  44              1              13
## 437           asymmetric  47              1              13
## 438           asymmetric  48              1              13
## 439           asymmetric  49              1              13
## 440           asymmetric  50              1              13
## 441           asymmetric  51              1              13
## 442           asymmetric  52              1              13
## 443           asymmetric  53              1              13
## 444           asymmetric  54              1              13
## 445           asymmetric  67              1              13
## 446           asymmetric  68              1              13
## 447           asymmetric  69              1              13
## 448           asymmetric  70              1              13
## 449           asymmetric  71              1              13
## 450           asymmetric  72              1              13
## 451           asymmetric  73              1              13
## 452           asymmetric  74              1              13
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## 454           asymmetric  78              1              13
## 455           asymmetric  79              1              13
## 456           asymmetric  80              1              13
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## 458           asymmetric  42              1              13
## 459           asymmetric  43              1              13
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## 462           asymmetric  48              1              13
## 463           asymmetric  49              1              13
## 464           asymmetric  50              1              13
## 465           asymmetric  51              1              13
## 466           asymmetric  52              1              13
## 467           asymmetric  53              1              13
## 468           asymmetric  54              1              13
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## 470           asymmetric  68              1              13
## 471           asymmetric  69              1              13
## 472           asymmetric  70              1              13
## 473           asymmetric  71              1              13
## 474           asymmetric  72              1              13
## 475           asymmetric  73              1              13
## 476           asymmetric  74              1              13
## 477           asymmetric  77              1              13
## 478           asymmetric  78              1              13
## 479           asymmetric  79              1              13
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## 485           asymmetric  47              1              13
## 486           asymmetric  48              1              13
## 487           asymmetric  49              1              13
## 488           asymmetric  50              1              13
## 489           asymmetric  51              1              13
## 490           asymmetric  52              1              13
## 491           asymmetric  53              1              13
## 492           asymmetric  54              1              13
## 493           asymmetric  67              1              13
## 494           asymmetric  68              1              13
## 495           asymmetric  69              1              13
## 496           asymmetric  70              1              13
## 497           asymmetric  71              1              13
## 498           asymmetric  72              1              13
## 499           asymmetric  73              1              13
## 500           asymmetric  74              1              13
## 501           asymmetric  77              1              13
## 502           asymmetric  78              1              13
## 503           asymmetric  79              1              13
## 504           asymmetric  80              1              13
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## 506           asymmetric  42              1              13
## 507           asymmetric  43              1              13
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## 510           asymmetric  48              1              13
## 511           asymmetric  49              1              13
## 512           asymmetric  50              1              13
## 513           asymmetric  51              1              13
## 514           asymmetric  52              1              13
## 515           asymmetric  53              1              13
## 516           asymmetric  54              1              13
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## 518           asymmetric  68              1              13
## 519           asymmetric  69              1              13
## 520           asymmetric  70              1              13
## 521           asymmetric  71              1              13
## 522           asymmetric  72              1              13
## 523           asymmetric  73              1              13
## 524           asymmetric  74              1              13
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## 526           asymmetric  78              1              13
## 527           asymmetric  79              1              13
## 528           asymmetric  80              1              13
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## 530           asymmetric  42              1              13
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## 536           asymmetric  50              1              13
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## 539           asymmetric  53              1              13
## 540           asymmetric  54              1              13
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## 542           asymmetric  68              1              13
## 543           asymmetric  69              1              13
## 544           asymmetric  70              1              13
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## 551           asymmetric  79              1              13
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## 660           asymmetric  60              1              14
## 661           asymmetric  61              1              14
## 662           asymmetric  62              1              14
## 663           asymmetric  63              1              14
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## 665           asymmetric  97              1              14
## 666           asymmetric  98              1              14
## 667           asymmetric  99              1              14
## 668           asymmetric 100              1              14
## 669           asymmetric 101              1              14
## 670           asymmetric 102              1              14
## 671           asymmetric 103              1              14
## 672           asymmetric 104              1              14
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## 675           asymmetric  19              1              14
## 676           asymmetric  20              1              14
## 677           asymmetric  21              1              14
## 678           asymmetric  22              1              14
## 679           asymmetric  23              1              14
## 680           asymmetric  24              1              14
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## 682           asymmetric  58              1              14
## 683           asymmetric  59              1              14
## 684           asymmetric  60              1              14
## 685           asymmetric  61              1              14
## 686           asymmetric  62              1              14
## 687           asymmetric  63              1              14
## 688           asymmetric  64              1              14
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## 690           asymmetric  98              1              14
## 691           asymmetric  99              1              14
## 692           asymmetric 100              1              14
## 693           asymmetric 101              1              14
## 694           asymmetric 102              1              14
## 695           asymmetric 103              1              14
## 696           asymmetric 104              1              14
## 697           asymmetric  17              1              14
## 698           asymmetric  18              1              14
## 699           asymmetric  19              1              14
## 700           asymmetric  20              1              14
## 701           asymmetric  21              1              14
## 702           asymmetric  22              1              14
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## 704           asymmetric  24              1              14
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## 706           asymmetric  58              1              14
## 707           asymmetric  59              1              14
## 708           asymmetric  60              1              14
## 709           asymmetric  61              1              14
## 710           asymmetric  62              1              14
## 711           asymmetric  63              1              14
## 712           asymmetric  64              1              14
## 713           asymmetric  97              1              14
## 714           asymmetric  98              1              14
## 715           asymmetric  99              1              14
## 716           asymmetric 100              1              14
## 717           asymmetric 101              1              14
## 718           asymmetric 102              1              14
## 719           asymmetric 103              1              14
## 720           asymmetric 104              1              14
## 721           asymmetric  17              1              14
## 722           asymmetric  18              1              14
## 723           asymmetric  19              1              14
## 724           asymmetric  20              1              14
## 725           asymmetric  21              1              14
## 726           asymmetric  22              1              14
## 727           asymmetric  23              1              14
## 728           asymmetric  24              1              14
## 729           asymmetric  57              1              14
## 730           asymmetric  58              1              14
## 731           asymmetric  59              1              14
## 732           asymmetric  60              1              14
## 733           asymmetric  61              1              14
## 734           asymmetric  62              1              14
## 735           asymmetric  63              1              14
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## 738           asymmetric  98              1              14
## 739           asymmetric  99              1              14
## 740           asymmetric 100              1              14
## 741           asymmetric 101              1              14
## 742           asymmetric 102              1              14
## 743           asymmetric 103              1              14
## 744           asymmetric 104              1              14
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## 746           asymmetric  18              1              14
## 747           asymmetric  19              1              14
## 748           asymmetric  20              1              14
## 749           asymmetric  21              1              14
## 750           asymmetric  22              1              14
## 751           asymmetric  23              1              14
## 752           asymmetric  24              1              14
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## 754           asymmetric  58              1              14
## 755           asymmetric  59              1              14
## 756           asymmetric  60              1              14
## 757           asymmetric  61              1              14
## 758           asymmetric  62              1              14
## 759           asymmetric  63              1              14
## 760           asymmetric  64              1              14
## 761           asymmetric  97              1              14
## 762           asymmetric  98              1              14
## 763           asymmetric  99              1              14
## 764           asymmetric 100              1              14
## 765           asymmetric 101              1              14
## 766           asymmetric 102              1              14
## 767           asymmetric 103              1              14
## 768           asymmetric 104              1              14
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## 771           asymmetric  15              1              15
## 772           asymmetric  16              1              15
## 773           asymmetric  25              1              15
## 774           asymmetric  26              1              15
## 775           asymmetric  35              1              15
## 776           asymmetric  36              1              15
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## 778           asymmetric  46              1              15
## 779           asymmetric  55              1              15
## 780           asymmetric  56              1              15
## 781           asymmetric  65              1              15
## 782           asymmetric  66              1              15
## 783           asymmetric  75              1              15
## 784           asymmetric  76              1              15
## 785           asymmetric  85              1              15
## 786           asymmetric  86              1              15
## 787           asymmetric  95              1              15
## 788           asymmetric  96              1              15
## 789           asymmetric 105              1              15
## 790           asymmetric 106              1              15
## 791           asymmetric 115              1              15
## 792           asymmetric 116              1              15
## 793           asymmetric   5              1              15
## 794           asymmetric   6              1              15
## 795           asymmetric  15              1              15
## 796           asymmetric  16              1              15
## 797           asymmetric  25              1              15
## 798           asymmetric  26              1              15
## 799           asymmetric  35              1              15
## 800           asymmetric  36              1              15
## 801           asymmetric  45              1              15
## 802           asymmetric  46              1              15
## 803           asymmetric  55              1              15
## 804           asymmetric  56              1              15
## 805           asymmetric  65              1              15
## 806           asymmetric  66              1              15
## 807           asymmetric  75              1              15
## 808           asymmetric  76              1              15
## 809           asymmetric  85              1              15
## 810           asymmetric  86              1              15
## 811           asymmetric  95              1              15
## 812           asymmetric  96              1              15
## 813           asymmetric 105              1              15
## 814           asymmetric 106              1              15
## 815           asymmetric 115              1              15
## 816           asymmetric 116              1              15
## 817           asymmetric   5              1              15
## 818           asymmetric   6              1              15
## 819           asymmetric  15              1              15
## 820           asymmetric  16              1              15
## 821           asymmetric  25              1              15
## 822           asymmetric  26              1              15
## 823           asymmetric  35              1              15
## 824           asymmetric  36              1              15
## 825           asymmetric  45              1              15
## 826           asymmetric  46              1              15
## 827           asymmetric  55              1              15
## 828           asymmetric  56              1              15
## 829           asymmetric  65              1              15
## 830           asymmetric  66              1              15
## 831           asymmetric  75              1              15
## 832           asymmetric  76              1              15
## 833           asymmetric  85              1              15
## 834           asymmetric  86              1              15
## 835           asymmetric  95              1              15
## 836           asymmetric  96              1              15
## 837           asymmetric 105              1              15
## 838           asymmetric 106              1              15
## 839           asymmetric 115              1              15
## 840           asymmetric 116              1              15
## 841           asymmetric   5              1              15
## 842           asymmetric   6              1              15
## 843           asymmetric  15              1              15
## 844           asymmetric  16              1              15
## 845           asymmetric  25              1              15
## 846           asymmetric  26              1              15
## 847           asymmetric  35              1              15
## 848           asymmetric  36              1              15
## 849           asymmetric  45              1              15
## 850           asymmetric  46              1              15
## 851           asymmetric  55              1              15
## 852           asymmetric  56              1              15
## 853           asymmetric  65              1              15
## 854           asymmetric  66              1              15
## 855           asymmetric  75              1              15
## 856           asymmetric  76              1              15
## 857           asymmetric  85              1              15
## 858           asymmetric  86              1              15
## 859           asymmetric  95              1              15
## 860           asymmetric  96              1              15
## 861           asymmetric 105              1              15
## 862           asymmetric 106              1              15
## 863           asymmetric 115              1              15
## 864           asymmetric 116              1              15
## 865           asymmetric   5              1              15
## 866           asymmetric   6              1              15
## 867           asymmetric  15              1              15
## 868           asymmetric  16              1              15
## 869           asymmetric  25              1              15
## 870           asymmetric  26              1              15
## 871           asymmetric  35              1              15
## 872           asymmetric  36              1              15
## 873           asymmetric  45              1              15
## 874           asymmetric  46              1              15
## 875           asymmetric  55              1              15
## 876           asymmetric  56              1              15
## 877           asymmetric  65              1              15
## 878           asymmetric  66              1              15
## 879           asymmetric  75              1              15
## 880           asymmetric  76              1              15
## 881           asymmetric  85              1              15
## 882           asymmetric  86              1              15
## 883           asymmetric  95              1              15
## 884           asymmetric  96              1              15
## 885           asymmetric 105              1              15
## 886           asymmetric 106              1              15
## 887           asymmetric 115              1              15
## 888           asymmetric 116              1              15
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## 890           asymmetric   6              1              15
## 891           asymmetric  15              1              15
## 892           asymmetric  16              1              15
## 893           asymmetric  25              1              15
## 894           asymmetric  26              1              15
## 895           asymmetric  35              1              15
## 896           asymmetric  36              1              15
## 897           asymmetric  45              1              15
## 898           asymmetric  46              1              15
## 899           asymmetric  55              1              15
## 900           asymmetric  56              1              15
## 901           asymmetric  65              1              15
## 902           asymmetric  66              1              15
## 903           asymmetric  75              1              15
## 904           asymmetric  76              1              15
## 905           asymmetric  85              1              15
## 906           asymmetric  86              1              15
## 907           asymmetric  95              1              15
## 908           asymmetric  96              1              15
## 909           asymmetric 105              1              15
## 910           asymmetric 106              1              15
## 911           asymmetric 115              1              15
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## 915           asymmetric  15              1              15
## 916           asymmetric  16              1              15
## 917           asymmetric  25              1              15
## 918           asymmetric  26              1              15
## 919           asymmetric  35              1              15
## 920           asymmetric  36              1              15
## 921           asymmetric  45              1              15
## 922           asymmetric  46              1              15
## 923           asymmetric  55              1              15
## 924           asymmetric  56              1              15
## 925           asymmetric  65              1              15
## 926           asymmetric  66              1              15
## 927           asymmetric  75              1              15
## 928           asymmetric  76              1              15
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## 930           asymmetric  86              1              15
## 931           asymmetric  95              1              15
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## 933           asymmetric 105              1              15
## 934           asymmetric 106              1              15
## 935           asymmetric 115              1              15
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## 939           asymmetric  15              1              15
## 940           asymmetric  16              1              15
## 941           asymmetric  25              1              15
## 942           asymmetric  26              1              15
## 943           asymmetric  35              1              15
## 944           asymmetric  36              1              15
## 945           asymmetric  45              1              15
## 946           asymmetric  46              1              15
## 947           asymmetric  55              1              15
## 948           asymmetric  56              1              15
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## 950           asymmetric  66              1              15
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## 952           asymmetric  76              1              15
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## 954           asymmetric  86              1              15
## 955           asymmetric  95              1              15
## 956           asymmetric  96              1              15
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## 958           asymmetric 106              1              15
## 959           asymmetric 115              1              15
## 960           asymmetric 116              1              15
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## 965           asymmetric  19              2              11
## 966           asymmetric  20              2              11
## 967           asymmetric  21              2              11
## 968           asymmetric  22              2              11
## 969           asymmetric  23              2              11
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## 972           asymmetric  36              2              11
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## 974           asymmetric  42              2              11
## 975           asymmetric  43              2              11
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## 977           asymmetric  49              2              11
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## 980           asymmetric 112              2              11
## 981           asymmetric 113              2              11
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## 983           asymmetric 119              2              11
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## 989           asymmetric  19              2              11
## 990           asymmetric  20              2              11
## 991           asymmetric  21              2              11
## 992           asymmetric  22              2              11
## 993           asymmetric  23              2              11
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## 995           asymmetric  35              2              11
## 996           asymmetric  36              2              11
## 997           asymmetric  41              2              11
## 998           asymmetric  42              2              11
## 999           asymmetric  43              2              11
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## 1022          asymmetric  42              2              11
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## 1024          asymmetric  48              2              11
## 1025          asymmetric  49              2              11
## 1026          asymmetric  50              2              11
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## 1030          asymmetric 118              2              11
## 1031          asymmetric 119              2              11
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## 1037          asymmetric  19              2              11
## 1038          asymmetric  20              2              11
## 1039          asymmetric  21              2              11
## 1040          asymmetric  22              2              11
## 1041          asymmetric  23              2              11
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## 1043          asymmetric  35              2              11
## 1044          asymmetric  36              2              11
## 1045          asymmetric  41              2              11
## 1046          asymmetric  42              2              11
## 1047          asymmetric  43              2              11
## 1048          asymmetric  48              2              11
## 1049          asymmetric  49              2              11
## 1050          asymmetric  50              2              11
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## 1053          asymmetric 113              2              11
## 1054          asymmetric 118              2              11
## 1055          asymmetric 119              2              11
## 1056          asymmetric 120              2              11
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## 1059          asymmetric  15              2              11
## 1060          asymmetric  18              2              11
## 1061          asymmetric  19              2              11
## 1062          asymmetric  20              2              11
## 1063          asymmetric  21              2              11
## 1064          asymmetric  22              2              11
## 1065          asymmetric  23              2              11
## 1066          asymmetric  26              2              11
## 1067          asymmetric  35              2              11
## 1068          asymmetric  36              2              11
## 1069          asymmetric  41              2              11
## 1070          asymmetric  42              2              11
## 1071          asymmetric  43              2              11
## 1072          asymmetric  48              2              11
## 1073          asymmetric  49              2              11
## 1074          asymmetric  50              2              11
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## 1076          asymmetric 112              2              11
## 1077          asymmetric 113              2              11
## 1078          asymmetric 118              2              11
## 1079          asymmetric 119              2              11
## 1080          asymmetric 120              2              11
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## 1083          asymmetric  15              2              11
## 1084          asymmetric  18              2              11
## 1085          asymmetric  19              2              11
## 1086          asymmetric  20              2              11
## 1087          asymmetric  21              2              11
## 1088          asymmetric  22              2              11
## 1089          asymmetric  23              2              11
## 1090          asymmetric  26              2              11
## 1091          asymmetric  35              2              11
## 1092          asymmetric  36              2              11
## 1093          asymmetric  41              2              11
## 1094          asymmetric  42              2              11
## 1095          asymmetric  43              2              11
## 1096          asymmetric  48              2              11
## 1097          asymmetric  49              2              11
## 1098          asymmetric  50              2              11
## 1099          asymmetric 111              2              11
## 1100          asymmetric 112              2              11
## 1101          asymmetric 113              2              11
## 1102          asymmetric 118              2              11
## 1103          asymmetric 119              2              11
## 1104          asymmetric 120              2              11
## 1105          asymmetric   5              2              11
## 1106          asymmetric   6              2              11
## 1107          asymmetric  15              2              11
## 1108          asymmetric  18              2              11
## 1109          asymmetric  19              2              11
## 1110          asymmetric  20              2              11
## 1111          asymmetric  21              2              11
## 1112          asymmetric  22              2              11
## 1113          asymmetric  23              2              11
## 1114          asymmetric  26              2              11
## 1115          asymmetric  35              2              11
## 1116          asymmetric  36              2              11
## 1117          asymmetric  41              2              11
## 1118          asymmetric  42              2              11
## 1119          asymmetric  43              2              11
## 1120          asymmetric  48              2              11
## 1121          asymmetric  49              2              11
## 1122          asymmetric  50              2              11
## 1123          asymmetric 111              2              11
## 1124          asymmetric 112              2              11
## 1125          asymmetric 113              2              11
## 1126          asymmetric 118              2              11
## 1127          asymmetric 119              2              11
## 1128          asymmetric 120              2              11
## 1129          asymmetric   5              2              11
## 1130          asymmetric   6              2              11
## 1131          asymmetric  15              2              11
## 1132          asymmetric  18              2              11
## 1133          asymmetric  19              2              11
## 1134          asymmetric  20              2              11
## 1135          asymmetric  21              2              11
## 1136          asymmetric  22              2              11
## 1137          asymmetric  23              2              11
## 1138          asymmetric  26              2              11
## 1139          asymmetric  35              2              11
## 1140          asymmetric  36              2              11
## 1141          asymmetric  41              2              11
## 1142          asymmetric  42              2              11
## 1143          asymmetric  43              2              11
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## 1145          asymmetric  49              2              11
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## 1148          asymmetric 112              2              11
## 1149          asymmetric 113              2              11
## 1150          asymmetric 118              2              11
## 1151          asymmetric 119              2              11
## 1152          asymmetric 120              2              11
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## 1155          asymmetric   3              2              12
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## 1157          asymmetric   9              2              12
## 1158          asymmetric  10              2              12
## 1159          asymmetric  71              2              12
## 1160          asymmetric  72              2              12
## 1161          asymmetric  73              2              12
## 1162          asymmetric  78              2              12
## 1163          asymmetric  79              2              12
## 1164          asymmetric  80              2              12
## 1165          asymmetric  85              2              12
## 1166          asymmetric  86              2              12
## 1167          asymmetric  95              2              12
## 1168          asymmetric  98              2              12
## 1169          asymmetric  99              2              12
## 1170          asymmetric 100              2              12
## 1171          asymmetric 101              2              12
## 1172          asymmetric 102              2              12
## 1173          asymmetric 103              2              12
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## 1175          asymmetric 115              2              12
## 1176          asymmetric 116              2              12
## 1177          asymmetric   1              2              12
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## 1181          asymmetric   9              2              12
## 1182          asymmetric  10              2              12
## 1183          asymmetric  71              2              12
## 1184          asymmetric  72              2              12
## 1185          asymmetric  73              2              12
## 1186          asymmetric  78              2              12
## 1187          asymmetric  79              2              12
## 1188          asymmetric  80              2              12
## 1189          asymmetric  85              2              12
## 1190          asymmetric  86              2              12
## 1191          asymmetric  95              2              12
## 1192          asymmetric  98              2              12
## 1193          asymmetric  99              2              12
## 1194          asymmetric 100              2              12
## 1195          asymmetric 101              2              12
## 1196          asymmetric 102              2              12
## 1197          asymmetric 103              2              12
## 1198          asymmetric 106              2              12
## 1199          asymmetric 115              2              12
## 1200          asymmetric 116              2              12
## 1201          asymmetric   1              2              12
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## 1203          asymmetric   3              2              12
## 1204          asymmetric   8              2              12
## 1205          asymmetric   9              2              12
## 1206          asymmetric  10              2              12
## 1207          asymmetric  71              2              12
## 1208          asymmetric  72              2              12
## 1209          asymmetric  73              2              12
## 1210          asymmetric  78              2              12
## 1211          asymmetric  79              2              12
## 1212          asymmetric  80              2              12
## 1213          asymmetric  85              2              12
## 1214          asymmetric  86              2              12
## 1215          asymmetric  95              2              12
## 1216          asymmetric  98              2              12
## 1217          asymmetric  99              2              12
## 1218          asymmetric 100              2              12
## 1219          asymmetric 101              2              12
## 1220          asymmetric 102              2              12
## 1221          asymmetric 103              2              12
## 1222          asymmetric 106              2              12
## 1223          asymmetric 115              2              12
## 1224          asymmetric 116              2              12
## 1225          asymmetric   1              2              12
## 1226          asymmetric   2              2              12
## 1227          asymmetric   3              2              12
## 1228          asymmetric   8              2              12
## 1229          asymmetric   9              2              12
## 1230          asymmetric  10              2              12
## 1231          asymmetric  71              2              12
## 1232          asymmetric  72              2              12
## 1233          asymmetric  73              2              12
## 1234          asymmetric  78              2              12
## 1235          asymmetric  79              2              12
## 1236          asymmetric  80              2              12
## 1237          asymmetric  85              2              12
## 1238          asymmetric  86              2              12
## 1239          asymmetric  95              2              12
## 1240          asymmetric  98              2              12
## 1241          asymmetric  99              2              12
## 1242          asymmetric 100              2              12
## 1243          asymmetric 101              2              12
## 1244          asymmetric 102              2              12
## 1245          asymmetric 103              2              12
## 1246          asymmetric 106              2              12
## 1247          asymmetric 115              2              12
## 1248          asymmetric 116              2              12
## 1249          asymmetric   1              2              12
## 1250          asymmetric   2              2              12
## 1251          asymmetric   3              2              12
## 1252          asymmetric   8              2              12
## 1253          asymmetric   9              2              12
## 1254          asymmetric  10              2              12
## 1255          asymmetric  71              2              12
## 1256          asymmetric  72              2              12
## 1257          asymmetric  73              2              12
## 1258          asymmetric  78              2              12
## 1259          asymmetric  79              2              12
## 1260          asymmetric  80              2              12
## 1261          asymmetric  85              2              12
## 1262          asymmetric  86              2              12
## 1263          asymmetric  95              2              12
## 1264          asymmetric  98              2              12
## 1265          asymmetric  99              2              12
## 1266          asymmetric 100              2              12
## 1267          asymmetric 101              2              12
## 1268          asymmetric 102              2              12
## 1269          asymmetric 103              2              12
## 1270          asymmetric 106              2              12
## 1271          asymmetric 115              2              12
## 1272          asymmetric 116              2              12
## 1273          asymmetric   1              2              12
## 1274          asymmetric   2              2              12
## 1275          asymmetric   3              2              12
## 1276          asymmetric   8              2              12
## 1277          asymmetric   9              2              12
## 1278          asymmetric  10              2              12
## 1279          asymmetric  71              2              12
## 1280          asymmetric  72              2              12
## 1281          asymmetric  73              2              12
## 1282          asymmetric  78              2              12
## 1283          asymmetric  79              2              12
## 1284          asymmetric  80              2              12
## 1285          asymmetric  85              2              12
## 1286          asymmetric  86              2              12
## 1287          asymmetric  95              2              12
## 1288          asymmetric  98              2              12
## 1289          asymmetric  99              2              12
## 1290          asymmetric 100              2              12
## 1291          asymmetric 101              2              12
## 1292          asymmetric 102              2              12
## 1293          asymmetric 103              2              12
## 1294          asymmetric 106              2              12
## 1295          asymmetric 115              2              12
## 1296          asymmetric 116              2              12
## 1297          asymmetric   1              2              12
## 1298          asymmetric   2              2              12
## 1299          asymmetric   3              2              12
## 1300          asymmetric   8              2              12
## 1301          asymmetric   9              2              12
## 1302          asymmetric  10              2              12
## 1303          asymmetric  71              2              12
## 1304          asymmetric  72              2              12
## 1305          asymmetric  73              2              12
## 1306          asymmetric  78              2              12
## 1307          asymmetric  79              2              12
## 1308          asymmetric  80              2              12
## 1309          asymmetric  85              2              12
## 1310          asymmetric  86              2              12
## 1311          asymmetric  95              2              12
## 1312          asymmetric  98              2              12
## 1313          asymmetric  99              2              12
## 1314          asymmetric 100              2              12
## 1315          asymmetric 101              2              12
## 1316          asymmetric 102              2              12
## 1317          asymmetric 103              2              12
## 1318          asymmetric 106              2              12
## 1319          asymmetric 115              2              12
## 1320          asymmetric 116              2              12
## 1321          asymmetric   1              2              12
## 1322          asymmetric   2              2              12
## 1323          asymmetric   3              2              12
## 1324          asymmetric   8              2              12
## 1325          asymmetric   9              2              12
## 1326          asymmetric  10              2              12
## 1327          asymmetric  71              2              12
## 1328          asymmetric  72              2              12
## 1329          asymmetric  73              2              12
## 1330          asymmetric  78              2              12
## 1331          asymmetric  79              2              12
## 1332          asymmetric  80              2              12
## 1333          asymmetric  85              2              12
## 1334          asymmetric  86              2              12
## 1335          asymmetric  95              2              12
## 1336          asymmetric  98              2              12
## 1337          asymmetric  99              2              12
## 1338          asymmetric 100              2              12
## 1339          asymmetric 101              2              12
## 1340          asymmetric 102              2              12
## 1341          asymmetric 103              2              12
## 1342          asymmetric 106              2              12
## 1343          asymmetric 115              2              12
## 1344          asymmetric 116              2              12
## 1345          asymmetric  31              2              13
## 1346          asymmetric  32              2              13
## 1347          asymmetric  33              2              13
## 1348          asymmetric  38              2              13
## 1349          asymmetric  39              2              13
## 1350          asymmetric  40              2              13
## 1351          asymmetric  45              2              13
## 1352          asymmetric  46              2              13
## 1353          asymmetric  55              2              13
## 1354          asymmetric  58              2              13
## 1355          asymmetric  59              2              13
## 1356          asymmetric  60              2              13
## 1357          asymmetric  61              2              13
## 1358          asymmetric  62              2              13
## 1359          asymmetric  63              2              13
## 1360          asymmetric  66              2              13
## 1361          asymmetric  75              2              13
## 1362          asymmetric  76              2              13
## 1363          asymmetric  81              2              13
## 1364          asymmetric  82              2              13
## 1365          asymmetric  83              2              13
## 1366          asymmetric  88              2              13
## 1367          asymmetric  89              2              13
## 1368          asymmetric  90              2              13
## 1369          asymmetric  31              2              13
## 1370          asymmetric  32              2              13
## 1371          asymmetric  33              2              13
## 1372          asymmetric  38              2              13
## 1373          asymmetric  39              2              13
## 1374          asymmetric  40              2              13
## 1375          asymmetric  45              2              13
## 1376          asymmetric  46              2              13
## 1377          asymmetric  55              2              13
## 1378          asymmetric  58              2              13
## 1379          asymmetric  59              2              13
## 1380          asymmetric  60              2              13
## 1381          asymmetric  61              2              13
## 1382          asymmetric  62              2              13
## 1383          asymmetric  63              2              13
## 1384          asymmetric  66              2              13
## 1385          asymmetric  75              2              13
## 1386          asymmetric  76              2              13
## 1387          asymmetric  81              2              13
## 1388          asymmetric  82              2              13
## 1389          asymmetric  83              2              13
## 1390          asymmetric  88              2              13
## 1391          asymmetric  89              2              13
## 1392          asymmetric  90              2              13
## 1393          asymmetric  31              2              13
## 1394          asymmetric  32              2              13
## 1395          asymmetric  33              2              13
## 1396          asymmetric  38              2              13
## 1397          asymmetric  39              2              13
## 1398          asymmetric  40              2              13
## 1399          asymmetric  45              2              13
## 1400          asymmetric  46              2              13
## 1401          asymmetric  55              2              13
## 1402          asymmetric  58              2              13
## 1403          asymmetric  59              2              13
## 1404          asymmetric  60              2              13
## 1405          asymmetric  61              2              13
## 1406          asymmetric  62              2              13
## 1407          asymmetric  63              2              13
## 1408          asymmetric  66              2              13
## 1409          asymmetric  75              2              13
## 1410          asymmetric  76              2              13
## 1411          asymmetric  81              2              13
## 1412          asymmetric  82              2              13
## 1413          asymmetric  83              2              13
## 1414          asymmetric  88              2              13
## 1415          asymmetric  89              2              13
## 1416          asymmetric  90              2              13
## 1417          asymmetric  31              2              13
## 1418          asymmetric  32              2              13
## 1419          asymmetric  33              2              13
## 1420          asymmetric  38              2              13
## 1421          asymmetric  39              2              13
## 1422          asymmetric  40              2              13
## 1423          asymmetric  45              2              13
## 1424          asymmetric  46              2              13
## 1425          asymmetric  55              2              13
## 1426          asymmetric  58              2              13
## 1427          asymmetric  59              2              13
## 1428          asymmetric  60              2              13
## 1429          asymmetric  61              2              13
## 1430          asymmetric  62              2              13
## 1431          asymmetric  63              2              13
## 1432          asymmetric  66              2              13
## 1433          asymmetric  75              2              13
## 1434          asymmetric  76              2              13
## 1435          asymmetric  81              2              13
## 1436          asymmetric  82              2              13
## 1437          asymmetric  83              2              13
## 1438          asymmetric  88              2              13
## 1439          asymmetric  89              2              13
## 1440          asymmetric  90              2              13
## 1441          asymmetric  31              2              13
## 1442          asymmetric  32              2              13
## 1443          asymmetric  33              2              13
## 1444          asymmetric  38              2              13
## 1445          asymmetric  39              2              13
## 1446          asymmetric  40              2              13
## 1447          asymmetric  45              2              13
## 1448          asymmetric  46              2              13
## 1449          asymmetric  55              2              13
## 1450          asymmetric  58              2              13
## 1451          asymmetric  59              2              13
## 1452          asymmetric  60              2              13
## 1453          asymmetric  61              2              13
## 1454          asymmetric  62              2              13
## 1455          asymmetric  63              2              13
## 1456          asymmetric  66              2              13
## 1457          asymmetric  75              2              13
## 1458          asymmetric  76              2              13
## 1459          asymmetric  81              2              13
## 1460          asymmetric  82              2              13
## 1461          asymmetric  83              2              13
## 1462          asymmetric  88              2              13
## 1463          asymmetric  89              2              13
## 1464          asymmetric  90              2              13
## 1465          asymmetric  31              2              13
## 1466          asymmetric  32              2              13
## 1467          asymmetric  33              2              13
## 1468          asymmetric  38              2              13
## 1469          asymmetric  39              2              13
## 1470          asymmetric  40              2              13
## 1471          asymmetric  45              2              13
## 1472          asymmetric  46              2              13
## 1473          asymmetric  55              2              13
## 1474          asymmetric  58              2              13
## 1475          asymmetric  59              2              13
## 1476          asymmetric  60              2              13
## 1477          asymmetric  61              2              13
## 1478          asymmetric  62              2              13
## 1479          asymmetric  63              2              13
## 1480          asymmetric  66              2              13
## 1481          asymmetric  75              2              13
## 1482          asymmetric  76              2              13
## 1483          asymmetric  81              2              13
## 1484          asymmetric  82              2              13
## 1485          asymmetric  83              2              13
## 1486          asymmetric  88              2              13
## 1487          asymmetric  89              2              13
## 1488          asymmetric  90              2              13
## 1489          asymmetric  31              2              13
## 1490          asymmetric  32              2              13
## 1491          asymmetric  33              2              13
## 1492          asymmetric  38              2              13
## 1493          asymmetric  39              2              13
## 1494          asymmetric  40              2              13
## 1495          asymmetric  45              2              13
## 1496          asymmetric  46              2              13
## 1497          asymmetric  55              2              13
## 1498          asymmetric  58              2              13
## 1499          asymmetric  59              2              13
## 1500          asymmetric  60              2              13
## 1501          asymmetric  61              2              13
## 1502          asymmetric  62              2              13
## 1503          asymmetric  63              2              13
## 1504          asymmetric  66              2              13
## 1505          asymmetric  75              2              13
## 1506          asymmetric  76              2              13
## 1507          asymmetric  81              2              13
## 1508          asymmetric  82              2              13
## 1509          asymmetric  83              2              13
## 1510          asymmetric  88              2              13
## 1511          asymmetric  89              2              13
## 1512          asymmetric  90              2              13
## 1513          asymmetric  31              2              13
## 1514          asymmetric  32              2              13
## 1515          asymmetric  33              2              13
## 1516          asymmetric  38              2              13
## 1517          asymmetric  39              2              13
## 1518          asymmetric  40              2              13
## 1519          asymmetric  45              2              13
## 1520          asymmetric  46              2              13
## 1521          asymmetric  55              2              13
## 1522          asymmetric  58              2              13
## 1523          asymmetric  59              2              13
## 1524          asymmetric  60              2              13
## 1525          asymmetric  61              2              13
## 1526          asymmetric  62              2              13
## 1527          asymmetric  63              2              13
## 1528          asymmetric  66              2              13
## 1529          asymmetric  75              2              13
## 1530          asymmetric  76              2              13
## 1531          asymmetric  81              2              13
## 1532          asymmetric  82              2              13
## 1533          asymmetric  83              2              13
## 1534          asymmetric  88              2              13
## 1535          asymmetric  89              2              13
## 1536          asymmetric  90              2              13
## 1537          asymmetric  11              2              14
## 1538          asymmetric  12              2              14
## 1539          asymmetric  13              2              14
## 1540          asymmetric  16              2              14
## 1541          asymmetric  25              2              14
## 1542          asymmetric  28              2              14
## 1543          asymmetric  29              2              14
## 1544          asymmetric  30              2              14
## 1545          asymmetric  51              2              14
## 1546          asymmetric  52              2              14
## 1547          asymmetric  53              2              14
## 1548          asymmetric  56              2              14
## 1549          asymmetric  65              2              14
## 1550          asymmetric  68              2              14
## 1551          asymmetric  69              2              14
## 1552          asymmetric  70              2              14
## 1553          asymmetric  91              2              14
## 1554          asymmetric  92              2              14
## 1555          asymmetric  93              2              14
## 1556          asymmetric  96              2              14
## 1557          asymmetric 105              2              14
## 1558          asymmetric 108              2              14
## 1559          asymmetric 109              2              14
## 1560          asymmetric 110              2              14
## 1561          asymmetric  11              2              14
## 1562          asymmetric  12              2              14
## 1563          asymmetric  13              2              14
## 1564          asymmetric  16              2              14
## 1565          asymmetric  25              2              14
## 1566          asymmetric  28              2              14
## 1567          asymmetric  29              2              14
## 1568          asymmetric  30              2              14
## 1569          asymmetric  51              2              14
## 1570          asymmetric  52              2              14
## 1571          asymmetric  53              2              14
## 1572          asymmetric  56              2              14
## 1573          asymmetric  65              2              14
## 1574          asymmetric  68              2              14
## 1575          asymmetric  69              2              14
## 1576          asymmetric  70              2              14
## 1577          asymmetric  91              2              14
## 1578          asymmetric  92              2              14
## 1579          asymmetric  93              2              14
## 1580          asymmetric  96              2              14
## 1581          asymmetric 105              2              14
## 1582          asymmetric 108              2              14
## 1583          asymmetric 109              2              14
## 1584          asymmetric 110              2              14
## 1585          asymmetric  11              2              14
## 1586          asymmetric  12              2              14
## 1587          asymmetric  13              2              14
## 1588          asymmetric  16              2              14
## 1589          asymmetric  25              2              14
## 1590          asymmetric  28              2              14
## 1591          asymmetric  29              2              14
## 1592          asymmetric  30              2              14
## 1593          asymmetric  51              2              14
## 1594          asymmetric  52              2              14
## 1595          asymmetric  53              2              14
## 1596          asymmetric  56              2              14
## 1597          asymmetric  65              2              14
## 1598          asymmetric  68              2              14
## 1599          asymmetric  69              2              14
## 1600          asymmetric  70              2              14
## 1601          asymmetric  91              2              14
## 1602          asymmetric  92              2              14
## 1603          asymmetric  93              2              14
## 1604          asymmetric  96              2              14
## 1605          asymmetric 105              2              14
## 1606          asymmetric 108              2              14
## 1607          asymmetric 109              2              14
## 1608          asymmetric 110              2              14
## 1609          asymmetric  11              2              14
## 1610          asymmetric  12              2              14
## 1611          asymmetric  13              2              14
## 1612          asymmetric  16              2              14
## 1613          asymmetric  25              2              14
## 1614          asymmetric  28              2              14
## 1615          asymmetric  29              2              14
## 1616          asymmetric  30              2              14
## 1617          asymmetric  51              2              14
## 1618          asymmetric  52              2              14
## 1619          asymmetric  53              2              14
## 1620          asymmetric  56              2              14
## 1621          asymmetric  65              2              14
## 1622          asymmetric  68              2              14
## 1623          asymmetric  69              2              14
## 1624          asymmetric  70              2              14
## 1625          asymmetric  91              2              14
## 1626          asymmetric  92              2              14
## 1627          asymmetric  93              2              14
## 1628          asymmetric  96              2              14
## 1629          asymmetric 105              2              14
## 1630          asymmetric 108              2              14
## 1631          asymmetric 109              2              14
## 1632          asymmetric 110              2              14
## 1633          asymmetric  11              2              14
## 1634          asymmetric  12              2              14
## 1635          asymmetric  13              2              14
## 1636          asymmetric  16              2              14
## 1637          asymmetric  25              2              14
## 1638          asymmetric  28              2              14
## 1639          asymmetric  29              2              14
## 1640          asymmetric  30              2              14
## 1641          asymmetric  51              2              14
## 1642          asymmetric  52              2              14
## 1643          asymmetric  53              2              14
## 1644          asymmetric  56              2              14
## 1645          asymmetric  65              2              14
## 1646          asymmetric  68              2              14
## 1647          asymmetric  69              2              14
## 1648          asymmetric  70              2              14
## 1649          asymmetric  91              2              14
## 1650          asymmetric  92              2              14
## 1651          asymmetric  93              2              14
## 1652          asymmetric  96              2              14
## 1653          asymmetric 105              2              14
## 1654          asymmetric 108              2              14
## 1655          asymmetric 109              2              14
## 1656          asymmetric 110              2              14
## 1657          asymmetric  11              2              14
## 1658          asymmetric  12              2              14
## 1659          asymmetric  13              2              14
## 1660          asymmetric  16              2              14
## 1661          asymmetric  25              2              14
## 1662          asymmetric  28              2              14
## 1663          asymmetric  29              2              14
## 1664          asymmetric  30              2              14
## 1665          asymmetric  51              2              14
## 1666          asymmetric  52              2              14
## 1667          asymmetric  53              2              14
## 1668          asymmetric  56              2              14
## 1669          asymmetric  65              2              14
## 1670          asymmetric  68              2              14
## 1671          asymmetric  69              2              14
## 1672          asymmetric  70              2              14
## 1673          asymmetric  91              2              14
## 1674          asymmetric  92              2              14
## 1675          asymmetric  93              2              14
## 1676          asymmetric  96              2              14
## 1677          asymmetric 105              2              14
## 1678          asymmetric 108              2              14
## 1679          asymmetric 109              2              14
## 1680          asymmetric 110              2              14
## 1681          asymmetric  11              2              14
## 1682          asymmetric  12              2              14
## 1683          asymmetric  13              2              14
## 1684          asymmetric  16              2              14
## 1685          asymmetric  25              2              14
## 1686          asymmetric  28              2              14
## 1687          asymmetric  29              2              14
## 1688          asymmetric  30              2              14
## 1689          asymmetric  51              2              14
## 1690          asymmetric  52              2              14
## 1691          asymmetric  53              2              14
## 1692          asymmetric  56              2              14
## 1693          asymmetric  65              2              14
## 1694          asymmetric  68              2              14
## 1695          asymmetric  69              2              14
## 1696          asymmetric  70              2              14
## 1697          asymmetric  91              2              14
## 1698          asymmetric  92              2              14
## 1699          asymmetric  93              2              14
## 1700          asymmetric  96              2              14
## 1701          asymmetric 105              2              14
## 1702          asymmetric 108              2              14
## 1703          asymmetric 109              2              14
## 1704          asymmetric 110              2              14
## 1705          asymmetric  11              2              14
## 1706          asymmetric  12              2              14
## 1707          asymmetric  13              2              14
## 1708          asymmetric  16              2              14
## 1709          asymmetric  25              2              14
## 1710          asymmetric  28              2              14
## 1711          asymmetric  29              2              14
## 1712          asymmetric  30              2              14
## 1713          asymmetric  51              2              14
## 1714          asymmetric  52              2              14
## 1715          asymmetric  53              2              14
## 1716          asymmetric  56              2              14
## 1717          asymmetric  65              2              14
## 1718          asymmetric  68              2              14
## 1719          asymmetric  69              2              14
## 1720          asymmetric  70              2              14
## 1721          asymmetric  91              2              14
## 1722          asymmetric  92              2              14
## 1723          asymmetric  93              2              14
## 1724          asymmetric  96              2              14
## 1725          asymmetric 105              2              14
## 1726          asymmetric 108              2              14
## 1727          asymmetric 109              2              14
## 1728          asymmetric 110              2              14
## 1729          asymmetric   4              2              15
## 1730          asymmetric   7              2              15
## 1731          asymmetric  14              2              15
## 1732          asymmetric  17              2              15
## 1733          asymmetric  24              2              15
## 1734          asymmetric  27              2              15
## 1735          asymmetric  34              2              15
## 1736          asymmetric  37              2              15
## 1737          asymmetric  44              2              15
## 1738          asymmetric  47              2              15
## 1739          asymmetric  54              2              15
## 1740          asymmetric  57              2              15
## 1741          asymmetric  64              2              15
## 1742          asymmetric  67              2              15
## 1743          asymmetric  74              2              15
## 1744          asymmetric  77              2              15
## 1745          asymmetric  84              2              15
## 1746          asymmetric  87              2              15
## 1747          asymmetric  94              2              15
## 1748          asymmetric  97              2              15
## 1749          asymmetric 104              2              15
## 1750          asymmetric 107              2              15
## 1751          asymmetric 114              2              15
## 1752          asymmetric 117              2              15
## 1753          asymmetric   4              2              15
## 1754          asymmetric   7              2              15
## 1755          asymmetric  14              2              15
## 1756          asymmetric  17              2              15
## 1757          asymmetric  24              2              15
## 1758          asymmetric  27              2              15
## 1759          asymmetric  34              2              15
## 1760          asymmetric  37              2              15
## 1761          asymmetric  44              2              15
## 1762          asymmetric  47              2              15
## 1763          asymmetric  54              2              15
## 1764          asymmetric  57              2              15
## 1765          asymmetric  64              2              15
## 1766          asymmetric  67              2              15
## 1767          asymmetric  74              2              15
## 1768          asymmetric  77              2              15
## 1769          asymmetric  84              2              15
## 1770          asymmetric  87              2              15
## 1771          asymmetric  94              2              15
## 1772          asymmetric  97              2              15
## 1773          asymmetric 104              2              15
## 1774          asymmetric 107              2              15
## 1775          asymmetric 114              2              15
## 1776          asymmetric 117              2              15
## 1777          asymmetric   4              2              15
## 1778          asymmetric   7              2              15
## 1779          asymmetric  14              2              15
## 1780          asymmetric  17              2              15
## 1781          asymmetric  24              2              15
## 1782          asymmetric  27              2              15
## 1783          asymmetric  34              2              15
## 1784          asymmetric  37              2              15
## 1785          asymmetric  44              2              15
## 1786          asymmetric  47              2              15
## 1787          asymmetric  54              2              15
## 1788          asymmetric  57              2              15
## 1789          asymmetric  64              2              15
## 1790          asymmetric  67              2              15
## 1791          asymmetric  74              2              15
## 1792          asymmetric  77              2              15
## 1793          asymmetric  84              2              15
## 1794          asymmetric  87              2              15
## 1795          asymmetric  94              2              15
## 1796          asymmetric  97              2              15
## 1797          asymmetric 104              2              15
## 1798          asymmetric 107              2              15
## 1799          asymmetric 114              2              15
## 1800          asymmetric 117              2              15
## 1801          asymmetric   4              2              15
## 1802          asymmetric   7              2              15
## 1803          asymmetric  14              2              15
## 1804          asymmetric  17              2              15
## 1805          asymmetric  24              2              15
## 1806          asymmetric  27              2              15
## 1807          asymmetric  34              2              15
## 1808          asymmetric  37              2              15
## 1809          asymmetric  44              2              15
## 1810          asymmetric  47              2              15
## 1811          asymmetric  54              2              15
## 1812          asymmetric  57              2              15
## 1813          asymmetric  64              2              15
## 1814          asymmetric  67              2              15
## 1815          asymmetric  74              2              15
## 1816          asymmetric  77              2              15
## 1817          asymmetric  84              2              15
## 1818          asymmetric  87              2              15
## 1819          asymmetric  94              2              15
## 1820          asymmetric  97              2              15
## 1821          asymmetric 104              2              15
## 1822          asymmetric 107              2              15
## 1823          asymmetric 114              2              15
## 1824          asymmetric 117              2              15
## 1825          asymmetric   4              2              15
## 1826          asymmetric   7              2              15
## 1827          asymmetric  14              2              15
## 1828          asymmetric  17              2              15
## 1829          asymmetric  24              2              15
## 1830          asymmetric  27              2              15
## 1831          asymmetric  34              2              15
## 1832          asymmetric  37              2              15
## 1833          asymmetric  44              2              15
## 1834          asymmetric  47              2              15
## 1835          asymmetric  54              2              15
## 1836          asymmetric  57              2              15
## 1837          asymmetric  64              2              15
## 1838          asymmetric  67              2              15
## 1839          asymmetric  74              2              15
## 1840          asymmetric  77              2              15
## 1841          asymmetric  84              2              15
## 1842          asymmetric  87              2              15
## 1843          asymmetric  94              2              15
## 1844          asymmetric  97              2              15
## 1845          asymmetric 104              2              15
## 1846          asymmetric 107              2              15
## 1847          asymmetric 114              2              15
## 1848          asymmetric 117              2              15
## 1849          asymmetric   4              2              15
## 1850          asymmetric   7              2              15
## 1851          asymmetric  14              2              15
## 1852          asymmetric  17              2              15
## 1853          asymmetric  24              2              15
## 1854          asymmetric  27              2              15
## 1855          asymmetric  34              2              15
## 1856          asymmetric  37              2              15
## 1857          asymmetric  44              2              15
## 1858          asymmetric  47              2              15
## 1859          asymmetric  54              2              15
## 1860          asymmetric  57              2              15
## 1861          asymmetric  64              2              15
## 1862          asymmetric  67              2              15
## 1863          asymmetric  74              2              15
## 1864          asymmetric  77              2              15
## 1865          asymmetric  84              2              15
## 1866          asymmetric  87              2              15
## 1867          asymmetric  94              2              15
## 1868          asymmetric  97              2              15
## 1869          asymmetric 104              2              15
## 1870          asymmetric 107              2              15
## 1871          asymmetric 114              2              15
## 1872          asymmetric 117              2              15
## 1873          asymmetric   4              2              15
## 1874          asymmetric   7              2              15
## 1875          asymmetric  14              2              15
## 1876          asymmetric  17              2              15
## 1877          asymmetric  24              2              15
## 1878          asymmetric  27              2              15
## 1879          asymmetric  34              2              15
## 1880          asymmetric  37              2              15
## 1881          asymmetric  44              2              15
## 1882          asymmetric  47              2              15
## 1883          asymmetric  54              2              15
## 1884          asymmetric  57              2              15
## 1885          asymmetric  64              2              15
## 1886          asymmetric  67              2              15
## 1887          asymmetric  74              2              15
## 1888          asymmetric  77              2              15
## 1889          asymmetric  84              2              15
## 1890          asymmetric  87              2              15
## 1891          asymmetric  94              2              15
## 1892          asymmetric  97              2              15
## 1893          asymmetric 104              2              15
## 1894          asymmetric 107              2              15
## 1895          asymmetric 114              2              15
## 1896          asymmetric 117              2              15
## 1897          asymmetric   4              2              15
## 1898          asymmetric   7              2              15
## 1899          asymmetric  14              2              15
## 1900          asymmetric  17              2              15
## 1901          asymmetric  24              2              15
## 1902          asymmetric  27              2              15
## 1903          asymmetric  34              2              15
## 1904          asymmetric  37              2              15
## 1905          asymmetric  44              2              15
## 1906          asymmetric  47              2              15
## 1907          asymmetric  54              2              15
## 1908          asymmetric  57              2              15
## 1909          asymmetric  64              2              15
## 1910          asymmetric  67              2              15
## 1911          asymmetric  74              2              15
## 1912          asymmetric  77              2              15
## 1913          asymmetric  84              2              15
## 1914          asymmetric  87              2              15
## 1915          asymmetric  94              2              15
## 1916          asymmetric  97              2              15
## 1917          asymmetric 104              2              15
## 1918          asymmetric 107              2              15
## 1919          asymmetric 114              2              15
## 1920          asymmetric 117              2              15
## 1921          asymmetric  16              3              11
## 1922          asymmetric  17              3              11
## 1923          asymmetric  24              3              11
## 1924          asymmetric  25              3              11
## 1925          asymmetric  44              3              11
## 1926          asymmetric  45              3              11
## 1927          asymmetric  46              3              11
## 1928          asymmetric  47              3              11
## 1929          asymmetric  51              3              11
## 1930          asymmetric  52              3              11
## 1931          asymmetric  59              3              11
## 1932          asymmetric  60              3              11
## 1933          asymmetric  79              3              11
## 1934          asymmetric  80              3              11
## 1935          asymmetric  81              3              11
## 1936          asymmetric  82              3              11
## 1937          asymmetric 101              3              11
## 1938          asymmetric 102              3              11
## 1939          asymmetric 109              3              11
## 1940          asymmetric 110              3              11
## 1941          asymmetric 114              3              11
## 1942          asymmetric 115              3              11
## 1943          asymmetric 116              3              11
## 1944          asymmetric 117              3              11
## 1945          asymmetric  16              3              11
## 1946          asymmetric  17              3              11
## 1947          asymmetric  24              3              11
## 1948          asymmetric  25              3              11
## 1949          asymmetric  44              3              11
## 1950          asymmetric  45              3              11
## 1951          asymmetric  46              3              11
## 1952          asymmetric  47              3              11
## 1953          asymmetric  51              3              11
## 1954          asymmetric  52              3              11
## 1955          asymmetric  59              3              11
## 1956          asymmetric  60              3              11
## 1957          asymmetric  79              3              11
## 1958          asymmetric  80              3              11
## 1959          asymmetric  81              3              11
## 1960          asymmetric  82              3              11
## 1961          asymmetric 101              3              11
## 1962          asymmetric 102              3              11
## 1963          asymmetric 109              3              11
## 1964          asymmetric 110              3              11
## 1965          asymmetric 114              3              11
## 1966          asymmetric 115              3              11
## 1967          asymmetric 116              3              11
## 1968          asymmetric 117              3              11
## 1969          asymmetric  16              3              11
## 1970          asymmetric  17              3              11
## 1971          asymmetric  24              3              11
## 1972          asymmetric  25              3              11
## 1973          asymmetric  44              3              11
## 1974          asymmetric  45              3              11
## 1975          asymmetric  46              3              11
## 1976          asymmetric  47              3              11
## 1977          asymmetric  51              3              11
## 1978          asymmetric  52              3              11
## 1979          asymmetric  59              3              11
## 1980          asymmetric  60              3              11
## 1981          asymmetric  79              3              11
## 1982          asymmetric  80              3              11
## 1983          asymmetric  81              3              11
## 1984          asymmetric  82              3              11
## 1985          asymmetric 101              3              11
## 1986          asymmetric 102              3              11
## 1987          asymmetric 109              3              11
## 1988          asymmetric 110              3              11
## 1989          asymmetric 114              3              11
## 1990          asymmetric 115              3              11
## 1991          asymmetric 116              3              11
## 1992          asymmetric 117              3              11
## 1993          asymmetric  16              3              11
## 1994          asymmetric  17              3              11
## 1995          asymmetric  24              3              11
## 1996          asymmetric  25              3              11
## 1997          asymmetric  44              3              11
## 1998          asymmetric  45              3              11
## 1999          asymmetric  46              3              11
## 2000          asymmetric  47              3              11
## 2001          asymmetric  51              3              11
## 2002          asymmetric  52              3              11
## 2003          asymmetric  59              3              11
## 2004          asymmetric  60              3              11
## 2005          asymmetric  79              3              11
## 2006          asymmetric  80              3              11
## 2007          asymmetric  81              3              11
## 2008          asymmetric  82              3              11
## 2009          asymmetric 101              3              11
## 2010          asymmetric 102              3              11
## 2011          asymmetric 109              3              11
## 2012          asymmetric 110              3              11
## 2013          asymmetric 114              3              11
## 2014          asymmetric 115              3              11
## 2015          asymmetric 116              3              11
## 2016          asymmetric 117              3              11
## 2017          asymmetric  16              3              11
## 2018          asymmetric  17              3              11
## 2019          asymmetric  24              3              11
## 2020          asymmetric  25              3              11
## 2021          asymmetric  44              3              11
## 2022          asymmetric  45              3              11
## 2023          asymmetric  46              3              11
## 2024          asymmetric  47              3              11
## 2025          asymmetric  51              3              11
## 2026          asymmetric  52              3              11
## 2027          asymmetric  59              3              11
## 2028          asymmetric  60              3              11
## 2029          asymmetric  79              3              11
## 2030          asymmetric  80              3              11
## 2031          asymmetric  81              3              11
## 2032          asymmetric  82              3              11
## 2033          asymmetric 101              3              11
## 2034          asymmetric 102              3              11
## 2035          asymmetric 109              3              11
## 2036          asymmetric 110              3              11
## 2037          asymmetric 114              3              11
## 2038          asymmetric 115              3              11
## 2039          asymmetric 116              3              11
## 2040          asymmetric 117              3              11
## 2041          asymmetric  16              3              11
## 2042          asymmetric  17              3              11
## 2043          asymmetric  24              3              11
## 2044          asymmetric  25              3              11
## 2045          asymmetric  44              3              11
## 2046          asymmetric  45              3              11
## 2047          asymmetric  46              3              11
## 2048          asymmetric  47              3              11
## 2049          asymmetric  51              3              11
## 2050          asymmetric  52              3              11
## 2051          asymmetric  59              3              11
## 2052          asymmetric  60              3              11
## 2053          asymmetric  79              3              11
## 2054          asymmetric  80              3              11
## 2055          asymmetric  81              3              11
## 2056          asymmetric  82              3              11
## 2057          asymmetric 101              3              11
## 2058          asymmetric 102              3              11
## 2059          asymmetric 109              3              11
## 2060          asymmetric 110              3              11
## 2061          asymmetric 114              3              11
## 2062          asymmetric 115              3              11
## 2063          asymmetric 116              3              11
## 2064          asymmetric 117              3              11
## 2065          asymmetric  16              3              11
## 2066          asymmetric  17              3              11
## 2067          asymmetric  24              3              11
## 2068          asymmetric  25              3              11
## 2069          asymmetric  44              3              11
## 2070          asymmetric  45              3              11
## 2071          asymmetric  46              3              11
## 2072          asymmetric  47              3              11
## 2073          asymmetric  51              3              11
## 2074          asymmetric  52              3              11
## 2075          asymmetric  59              3              11
## 2076          asymmetric  60              3              11
## 2077          asymmetric  79              3              11
## 2078          asymmetric  80              3              11
## 2079          asymmetric  81              3              11
## 2080          asymmetric  82              3              11
## 2081          asymmetric 101              3              11
## 2082          asymmetric 102              3              11
## 2083          asymmetric 109              3              11
## 2084          asymmetric 110              3              11
## 2085          asymmetric 114              3              11
## 2086          asymmetric 115              3              11
## 2087          asymmetric 116              3              11
## 2088          asymmetric 117              3              11
## 2089          asymmetric  16              3              11
## 2090          asymmetric  17              3              11
## 2091          asymmetric  24              3              11
## 2092          asymmetric  25              3              11
## 2093          asymmetric  44              3              11
## 2094          asymmetric  45              3              11
## 2095          asymmetric  46              3              11
## 2096          asymmetric  47              3              11
## 2097          asymmetric  51              3              11
## 2098          asymmetric  52              3              11
## 2099          asymmetric  59              3              11
## 2100          asymmetric  60              3              11
## 2101          asymmetric  79              3              11
## 2102          asymmetric  80              3              11
## 2103          asymmetric  81              3              11
## 2104          asymmetric  82              3              11
## 2105          asymmetric 101              3              11
## 2106          asymmetric 102              3              11
## 2107          asymmetric 109              3              11
## 2108          asymmetric 110              3              11
## 2109          asymmetric 114              3              11
## 2110          asymmetric 115              3              11
## 2111          asymmetric 116              3              11
## 2112          asymmetric 117              3              11
## 2113          asymmetric   4              3              12
## 2114          asymmetric   5              3              12
## 2115          asymmetric   6              3              12
## 2116          asymmetric   7              3              12
## 2117          asymmetric  11              3              12
## 2118          asymmetric  12              3              12
## 2119          asymmetric  19              3              12
## 2120          asymmetric  20              3              12
## 2121          asymmetric  39              3              12
## 2122          asymmetric  40              3              12
## 2123          asymmetric  41              3              12
## 2124          asymmetric  42              3              12
## 2125          asymmetric  61              3              12
## 2126          asymmetric  62              3              12
## 2127          asymmetric  69              3              12
## 2128          asymmetric  70              3              12
## 2129          asymmetric  74              3              12
## 2130          asymmetric  75              3              12
## 2131          asymmetric  76              3              12
## 2132          asymmetric  77              3              12
## 2133          asymmetric  96              3              12
## 2134          asymmetric  97              3              12
## 2135          asymmetric 104              3              12
## 2136          asymmetric 105              3              12
## 2137          asymmetric   4              3              12
## 2138          asymmetric   5              3              12
## 2139          asymmetric   6              3              12
## 2140          asymmetric   7              3              12
## 2141          asymmetric  11              3              12
## 2142          asymmetric  12              3              12
## 2143          asymmetric  19              3              12
## 2144          asymmetric  20              3              12
## 2145          asymmetric  39              3              12
## 2146          asymmetric  40              3              12
## 2147          asymmetric  41              3              12
## 2148          asymmetric  42              3              12
## 2149          asymmetric  61              3              12
## 2150          asymmetric  62              3              12
## 2151          asymmetric  69              3              12
## 2152          asymmetric  70              3              12
## 2153          asymmetric  74              3              12
## 2154          asymmetric  75              3              12
## 2155          asymmetric  76              3              12
## 2156          asymmetric  77              3              12
## 2157          asymmetric  96              3              12
## 2158          asymmetric  97              3              12
## 2159          asymmetric 104              3              12
## 2160          asymmetric 105              3              12
## 2161          asymmetric   4              3              12
## 2162          asymmetric   5              3              12
## 2163          asymmetric   6              3              12
## 2164          asymmetric   7              3              12
## 2165          asymmetric  11              3              12
## 2166          asymmetric  12              3              12
## 2167          asymmetric  19              3              12
## 2168          asymmetric  20              3              12
## 2169          asymmetric  39              3              12
## 2170          asymmetric  40              3              12
## 2171          asymmetric  41              3              12
## 2172          asymmetric  42              3              12
## 2173          asymmetric  61              3              12
## 2174          asymmetric  62              3              12
## 2175          asymmetric  69              3              12
## 2176          asymmetric  70              3              12
## 2177          asymmetric  74              3              12
## 2178          asymmetric  75              3              12
## 2179          asymmetric  76              3              12
## 2180          asymmetric  77              3              12
## 2181          asymmetric  96              3              12
## 2182          asymmetric  97              3              12
## 2183          asymmetric 104              3              12
## 2184          asymmetric 105              3              12
## 2185          asymmetric   4              3              12
## 2186          asymmetric   5              3              12
## 2187          asymmetric   6              3              12
## 2188          asymmetric   7              3              12
## 2189          asymmetric  11              3              12
## 2190          asymmetric  12              3              12
## 2191          asymmetric  19              3              12
## 2192          asymmetric  20              3              12
## 2193          asymmetric  39              3              12
## 2194          asymmetric  40              3              12
## 2195          asymmetric  41              3              12
## 2196          asymmetric  42              3              12
## 2197          asymmetric  61              3              12
## 2198          asymmetric  62              3              12
## 2199          asymmetric  69              3              12
## 2200          asymmetric  70              3              12
## 2201          asymmetric  74              3              12
## 2202          asymmetric  75              3              12
## 2203          asymmetric  76              3              12
## 2204          asymmetric  77              3              12
## 2205          asymmetric  96              3              12
## 2206          asymmetric  97              3              12
## 2207          asymmetric 104              3              12
## 2208          asymmetric 105              3              12
## 2209          asymmetric   4              3              12
## 2210          asymmetric   5              3              12
## 2211          asymmetric   6              3              12
## 2212          asymmetric   7              3              12
## 2213          asymmetric  11              3              12
## 2214          asymmetric  12              3              12
## 2215          asymmetric  19              3              12
## 2216          asymmetric  20              3              12
## 2217          asymmetric  39              3              12
## 2218          asymmetric  40              3              12
## 2219          asymmetric  41              3              12
## 2220          asymmetric  42              3              12
## 2221          asymmetric  61              3              12
## 2222          asymmetric  62              3              12
## 2223          asymmetric  69              3              12
## 2224          asymmetric  70              3              12
## 2225          asymmetric  74              3              12
## 2226          asymmetric  75              3              12
## 2227          asymmetric  76              3              12
## 2228          asymmetric  77              3              12
## 2229          asymmetric  96              3              12
## 2230          asymmetric  97              3              12
## 2231          asymmetric 104              3              12
## 2232          asymmetric 105              3              12
## 2233          asymmetric   4              3              12
## 2234          asymmetric   5              3              12
## 2235          asymmetric   6              3              12
## 2236          asymmetric   7              3              12
## 2237          asymmetric  11              3              12
## 2238          asymmetric  12              3              12
## 2239          asymmetric  19              3              12
## 2240          asymmetric  20              3              12
## 2241          asymmetric  39              3              12
## 2242          asymmetric  40              3              12
## 2243          asymmetric  41              3              12
## 2244          asymmetric  42              3              12
## 2245          asymmetric  61              3              12
## 2246          asymmetric  62              3              12
## 2247          asymmetric  69              3              12
## 2248          asymmetric  70              3              12
## 2249          asymmetric  74              3              12
## 2250          asymmetric  75              3              12
## 2251          asymmetric  76              3              12
## 2252          asymmetric  77              3              12
## 2253          asymmetric  96              3              12
## 2254          asymmetric  97              3              12
## 2255          asymmetric 104              3              12
## 2256          asymmetric 105              3              12
## 2257          asymmetric   4              3              12
## 2258          asymmetric   5              3              12
## 2259          asymmetric   6              3              12
## 2260          asymmetric   7              3              12
## 2261          asymmetric  11              3              12
## 2262          asymmetric  12              3              12
## 2263          asymmetric  19              3              12
## 2264          asymmetric  20              3              12
## 2265          asymmetric  39              3              12
## 2266          asymmetric  40              3              12
## 2267          asymmetric  41              3              12
## 2268          asymmetric  42              3              12
## 2269          asymmetric  61              3              12
## 2270          asymmetric  62              3              12
## 2271          asymmetric  69              3              12
## 2272          asymmetric  70              3              12
## 2273          asymmetric  74              3              12
## 2274          asymmetric  75              3              12
## 2275          asymmetric  76              3              12
## 2276          asymmetric  77              3              12
## 2277          asymmetric  96              3              12
## 2278          asymmetric  97              3              12
## 2279          asymmetric 104              3              12
## 2280          asymmetric 105              3              12
## 2281          asymmetric   4              3              12
## 2282          asymmetric   5              3              12
## 2283          asymmetric   6              3              12
## 2284          asymmetric   7              3              12
## 2285          asymmetric  11              3              12
## 2286          asymmetric  12              3              12
## 2287          asymmetric  19              3              12
## 2288          asymmetric  20              3              12
## 2289          asymmetric  39              3              12
## 2290          asymmetric  40              3              12
## 2291          asymmetric  41              3              12
## 2292          asymmetric  42              3              12
## 2293          asymmetric  61              3              12
## 2294          asymmetric  62              3              12
## 2295          asymmetric  69              3              12
## 2296          asymmetric  70              3              12
## 2297          asymmetric  74              3              12
## 2298          asymmetric  75              3              12
## 2299          asymmetric  76              3              12
## 2300          asymmetric  77              3              12
## 2301          asymmetric  96              3              12
## 2302          asymmetric  97              3              12
## 2303          asymmetric 104              3              12
## 2304          asymmetric 105              3              12
## 2305          asymmetric   1              3              13
## 2306          asymmetric   2              3              13
## 2307          asymmetric  21              3              13
## 2308          asymmetric  22              3              13
## 2309          asymmetric  29              3              13
## 2310          asymmetric  30              3              13
## 2311          asymmetric  34              3              13
## 2312          asymmetric  35              3              13
## 2313          asymmetric  36              3              13
## 2314          asymmetric  37              3              13
## 2315          asymmetric  56              3              13
## 2316          asymmetric  57              3              13
## 2317          asymmetric  64              3              13
## 2318          asymmetric  65              3              13
## 2319          asymmetric  84              3              13
## 2320          asymmetric  85              3              13
## 2321          asymmetric  86              3              13
## 2322          asymmetric  87              3              13
## 2323          asymmetric  91              3              13
## 2324          asymmetric  92              3              13
## 2325          asymmetric  99              3              13
## 2326          asymmetric 100              3              13
## 2327          asymmetric 119              3              13
## 2328          asymmetric 120              3              13
## 2329          asymmetric   1              3              13
## 2330          asymmetric   2              3              13
## 2331          asymmetric  21              3              13
## 2332          asymmetric  22              3              13
## 2333          asymmetric  29              3              13
## 2334          asymmetric  30              3              13
## 2335          asymmetric  34              3              13
## 2336          asymmetric  35              3              13
## 2337          asymmetric  36              3              13
## 2338          asymmetric  37              3              13
## 2339          asymmetric  56              3              13
## 2340          asymmetric  57              3              13
## 2341          asymmetric  64              3              13
## 2342          asymmetric  65              3              13
## 2343          asymmetric  84              3              13
## 2344          asymmetric  85              3              13
## 2345          asymmetric  86              3              13
## 2346          asymmetric  87              3              13
## 2347          asymmetric  91              3              13
## 2348          asymmetric  92              3              13
## 2349          asymmetric  99              3              13
## 2350          asymmetric 100              3              13
## 2351          asymmetric 119              3              13
## 2352          asymmetric 120              3              13
## 2353          asymmetric   1              3              13
## 2354          asymmetric   2              3              13
## 2355          asymmetric  21              3              13
## 2356          asymmetric  22              3              13
## 2357          asymmetric  29              3              13
## 2358          asymmetric  30              3              13
## 2359          asymmetric  34              3              13
## 2360          asymmetric  35              3              13
## 2361          asymmetric  36              3              13
## 2362          asymmetric  37              3              13
## 2363          asymmetric  56              3              13
## 2364          asymmetric  57              3              13
## 2365          asymmetric  64              3              13
## 2366          asymmetric  65              3              13
## 2367          asymmetric  84              3              13
## 2368          asymmetric  85              3              13
## 2369          asymmetric  86              3              13
## 2370          asymmetric  87              3              13
## 2371          asymmetric  91              3              13
## 2372          asymmetric  92              3              13
## 2373          asymmetric  99              3              13
## 2374          asymmetric 100              3              13
## 2375          asymmetric 119              3              13
## 2376          asymmetric 120              3              13
## 2377          asymmetric   1              3              13
## 2378          asymmetric   2              3              13
## 2379          asymmetric  21              3              13
## 2380          asymmetric  22              3              13
## 2381          asymmetric  29              3              13
## 2382          asymmetric  30              3              13
## 2383          asymmetric  34              3              13
## 2384          asymmetric  35              3              13
## 2385          asymmetric  36              3              13
## 2386          asymmetric  37              3              13
## 2387          asymmetric  56              3              13
## 2388          asymmetric  57              3              13
## 2389          asymmetric  64              3              13
## 2390          asymmetric  65              3              13
## 2391          asymmetric  84              3              13
## 2392          asymmetric  85              3              13
## 2393          asymmetric  86              3              13
## 2394          asymmetric  87              3              13
## 2395          asymmetric  91              3              13
## 2396          asymmetric  92              3              13
## 2397          asymmetric  99              3              13
## 2398          asymmetric 100              3              13
## 2399          asymmetric 119              3              13
## 2400          asymmetric 120              3              13
## 2401          asymmetric   1              3              13
## 2402          asymmetric   2              3              13
## 2403          asymmetric  21              3              13
## 2404          asymmetric  22              3              13
## 2405          asymmetric  29              3              13
## 2406          asymmetric  30              3              13
## 2407          asymmetric  34              3              13
## 2408          asymmetric  35              3              13
## 2409          asymmetric  36              3              13
## 2410          asymmetric  37              3              13
## 2411          asymmetric  56              3              13
## 2412          asymmetric  57              3              13
## 2413          asymmetric  64              3              13
## 2414          asymmetric  65              3              13
## 2415          asymmetric  84              3              13
## 2416          asymmetric  85              3              13
## 2417          asymmetric  86              3              13
## 2418          asymmetric  87              3              13
## 2419          asymmetric  91              3              13
## 2420          asymmetric  92              3              13
## 2421          asymmetric  99              3              13
## 2422          asymmetric 100              3              13
## 2423          asymmetric 119              3              13
## 2424          asymmetric 120              3              13
## 2425          asymmetric   1              3              13
## 2426          asymmetric   2              3              13
## 2427          asymmetric  21              3              13
## 2428          asymmetric  22              3              13
## 2429          asymmetric  29              3              13
## 2430          asymmetric  30              3              13
## 2431          asymmetric  34              3              13
## 2432          asymmetric  35              3              13
## 2433          asymmetric  36              3              13
## 2434          asymmetric  37              3              13
## 2435          asymmetric  56              3              13
## 2436          asymmetric  57              3              13
## 2437          asymmetric  64              3              13
## 2438          asymmetric  65              3              13
## 2439          asymmetric  84              3              13
## 2440          asymmetric  85              3              13
## 2441          asymmetric  86              3              13
## 2442          asymmetric  87              3              13
## 2443          asymmetric  91              3              13
## 2444          asymmetric  92              3              13
## 2445          asymmetric  99              3              13
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## 3037          asymmetric  83              4              11
## 3038          asymmetric  84              4              11
## 3039          asymmetric  85              4              11
## 3040          asymmetric  90              4              11
## 3041          asymmetric  91              4              11
## 3042          asymmetric 100              4              11
## 3043          asymmetric 103              4              11
## 3044          asymmetric 104              4              11
## 3045          asymmetric 105              4              11
## 3046          asymmetric 106              4              11
## 3047          asymmetric 107              4              11
## 3048          asymmetric 108              4              11
## 3049          asymmetric  53              4              11
## 3050          asymmetric  54              4              11
## 3051          asymmetric  55              4              11
## 3052          asymmetric  56              4              11
## 3053          asymmetric  57              4              11
## 3054          asymmetric  58              4              11
## 3055          asymmetric  61              4              11
## 3056          asymmetric  70              4              11
## 3057          asymmetric  71              4              11
## 3058          asymmetric  76              4              11
## 3059          asymmetric  77              4              11
## 3060          asymmetric  78              4              11
## 3061          asymmetric  83              4              11
## 3062          asymmetric  84              4              11
## 3063          asymmetric  85              4              11
## 3064          asymmetric  90              4              11
## 3065          asymmetric  91              4              11
## 3066          asymmetric 100              4              11
## 3067          asymmetric 103              4              11
## 3068          asymmetric 104              4              11
## 3069          asymmetric 105              4              11
## 3070          asymmetric 106              4              11
## 3071          asymmetric 107              4              11
## 3072          asymmetric 108              4              11
## 3073          asymmetric  13              4              12
## 3074          asymmetric  14              4              12
## 3075          asymmetric  15              4              12
## 3076          asymmetric  16              4              12
## 3077          asymmetric  17              4              12
## 3078          asymmetric  18              4              12
## 3079          asymmetric  21              4              12
## 3080          asymmetric  30              4              12
## 3081          asymmetric  31              4              12
## 3082          asymmetric  36              4              12
## 3083          asymmetric  37              4              12
## 3084          asymmetric  38              4              12
## 3085          asymmetric  43              4              12
## 3086          asymmetric  44              4              12
## 3087          asymmetric  45              4              12
## 3088          asymmetric  50              4              12
## 3089          asymmetric  51              4              12
## 3090          asymmetric  60              4              12
## 3091          asymmetric  63              4              12
## 3092          asymmetric  64              4              12
## 3093          asymmetric  65              4              12
## 3094          asymmetric  66              4              12
## 3095          asymmetric  67              4              12
## 3096          asymmetric  68              4              12
## 3097          asymmetric  13              4              12
## 3098          asymmetric  14              4              12
## 3099          asymmetric  15              4              12
## 3100          asymmetric  16              4              12
## 3101          asymmetric  17              4              12
## 3102          asymmetric  18              4              12
## 3103          asymmetric  21              4              12
## 3104          asymmetric  30              4              12
## 3105          asymmetric  31              4              12
## 3106          asymmetric  36              4              12
## 3107          asymmetric  37              4              12
## 3108          asymmetric  38              4              12
## 3109          asymmetric  43              4              12
## 3110          asymmetric  44              4              12
## 3111          asymmetric  45              4              12
## 3112          asymmetric  50              4              12
## 3113          asymmetric  51              4              12
## 3114          asymmetric  60              4              12
## 3115          asymmetric  63              4              12
## 3116          asymmetric  64              4              12
## 3117          asymmetric  65              4              12
## 3118          asymmetric  66              4              12
## 3119          asymmetric  67              4              12
## 3120          asymmetric  68              4              12
## 3121          asymmetric  13              4              12
## 3122          asymmetric  14              4              12
## 3123          asymmetric  15              4              12
## 3124          asymmetric  16              4              12
## 3125          asymmetric  17              4              12
## 3126          asymmetric  18              4              12
## 3127          asymmetric  21              4              12
## 3128          asymmetric  30              4              12
## 3129          asymmetric  31              4              12
## 3130          asymmetric  36              4              12
## 3131          asymmetric  37              4              12
## 3132          asymmetric  38              4              12
## 3133          asymmetric  43              4              12
## 3134          asymmetric  44              4              12
## 3135          asymmetric  45              4              12
## 3136          asymmetric  50              4              12
## 3137          asymmetric  51              4              12
## 3138          asymmetric  60              4              12
## 3139          asymmetric  63              4              12
## 3140          asymmetric  64              4              12
## 3141          asymmetric  65              4              12
## 3142          asymmetric  66              4              12
## 3143          asymmetric  67              4              12
## 3144          asymmetric  68              4              12
## 3145          asymmetric  13              4              12
## 3146          asymmetric  14              4              12
## 3147          asymmetric  15              4              12
## 3148          asymmetric  16              4              12
## 3149          asymmetric  17              4              12
## 3150          asymmetric  18              4              12
## 3151          asymmetric  21              4              12
## 3152          asymmetric  30              4              12
## 3153          asymmetric  31              4              12
## 3154          asymmetric  36              4              12
## 3155          asymmetric  37              4              12
## 3156          asymmetric  38              4              12
## 3157          asymmetric  43              4              12
## 3158          asymmetric  44              4              12
## 3159          asymmetric  45              4              12
## 3160          asymmetric  50              4              12
## 3161          asymmetric  51              4              12
## 3162          asymmetric  60              4              12
## 3163          asymmetric  63              4              12
## 3164          asymmetric  64              4              12
## 3165          asymmetric  65              4              12
## 3166          asymmetric  66              4              12
## 3167          asymmetric  67              4              12
## 3168          asymmetric  68              4              12
## 3169          asymmetric  13              4              12
## 3170          asymmetric  14              4              12
## 3171          asymmetric  15              4              12
## 3172          asymmetric  16              4              12
## 3173          asymmetric  17              4              12
## 3174          asymmetric  18              4              12
## 3175          asymmetric  21              4              12
## 3176          asymmetric  30              4              12
## 3177          asymmetric  31              4              12
## 3178          asymmetric  36              4              12
## 3179          asymmetric  37              4              12
## 3180          asymmetric  38              4              12
## 3181          asymmetric  43              4              12
## 3182          asymmetric  44              4              12
## 3183          asymmetric  45              4              12
## 3184          asymmetric  50              4              12
## 3185          asymmetric  51              4              12
## 3186          asymmetric  60              4              12
## 3187          asymmetric  63              4              12
## 3188          asymmetric  64              4              12
## 3189          asymmetric  65              4              12
## 3190          asymmetric  66              4              12
## 3191          asymmetric  67              4              12
## 3192          asymmetric  68              4              12
## 3193          asymmetric  13              4              12
## 3194          asymmetric  14              4              12
## 3195          asymmetric  15              4              12
## 3196          asymmetric  16              4              12
## 3197          asymmetric  17              4              12
## 3198          asymmetric  18              4              12
## 3199          asymmetric  21              4              12
## 3200          asymmetric  30              4              12
## 3201          asymmetric  31              4              12
## 3202          asymmetric  36              4              12
## 3203          asymmetric  37              4              12
## 3204          asymmetric  38              4              12
## 3205          asymmetric  43              4              12
## 3206          asymmetric  44              4              12
## 3207          asymmetric  45              4              12
## 3208          asymmetric  50              4              12
## 3209          asymmetric  51              4              12
## 3210          asymmetric  60              4              12
## 3211          asymmetric  63              4              12
## 3212          asymmetric  64              4              12
## 3213          asymmetric  65              4              12
## 3214          asymmetric  66              4              12
## 3215          asymmetric  67              4              12
## 3216          asymmetric  68              4              12
## 3217          asymmetric  13              4              12
## 3218          asymmetric  14              4              12
## 3219          asymmetric  15              4              12
## 3220          asymmetric  16              4              12
## 3221          asymmetric  17              4              12
## 3222          asymmetric  18              4              12
## 3223          asymmetric  21              4              12
## 3224          asymmetric  30              4              12
## 3225          asymmetric  31              4              12
##  [ reached 'max' / getOption("max.print") -- omitted 6695 rows ]

Filter according to disturbance

Here I’m filtering patches to have only the ones with disturbance low.

#Filter data sets according to the global disturbance
ds_individuals = ds_individuals %>%
  filter(disturbance == disturbance_global_input)
ds_patches = ds_patches %>%
  filter(disturbance == disturbance_global_input)
ds_patches_effect_size = ds_patches_effect_size %>%
  filter(disturbance == disturbance_global_input)
ds_metaecosystems = ds_metaecosystems %>%
  filter(disturbance == disturbance_global_input)
ds_classes = ds_classes %>%
  filter(disturbance == disturbance_global_input)
ds_classes_effect_size = ds_classes_effect_size %>%
  filter(disturbance == disturbance_global_input)

Plots & Analysis

All Meta-ecosystems

metaecosystem_type_i = c("Small-Small meta-ecosystem",
                         "Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Large-Large meta-ecosystem",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")

Biomass

response_variable = "total_metaecosystem_bioarea_mm2"
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)

Alpha (mean Shannon)

response_variable = "mean_shannon"
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)

Beta (Bray-Curtis)

response_variable = "bray_curtis"
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)

Gamma (regional richness)

response_variable = "metaecosystem_richness"
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)

MM vs SL

Biomass

response_variable = "total_metaecosystem_bioarea_mm2"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Small-Large meta-ecosystem")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_symmetry and patch_size_symmetry : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry +
    patch_size_symmetry : day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: 
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +  
##     (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    612.0    628.8   -298.0    596.0       52 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.78  -0.53   0.01   0.55   2.56 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 1e+02    10.5          
##            day         3e-02     0.2     -1.00
##  Residual              1e+03    34.0          
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                                  Estimate Std. Error    df t value Pr(>|t|)    
## (Intercept)                         233.9       18.1  24.3    12.9    2e-12 ***
## day                                  -6.7        0.9  45.7    -7.4    3e-09 ***
## patch_size_symmetrysymmetric         37.6       25.6  24.3     1.5      0.2    
## day:patch_size_symmetrysymmetric     -1.1        1.3  45.7    -0.9      0.4    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.920              
## ptch_sz_sym -0.707  0.651       
## dy:ptch_sz_  0.651 -0.707 -0.920
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.9   0.207     none

 
 
 

Reduced model (with patch_size_symmetry)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    610.8    625.4   -298.4    596.8       53 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.74  -0.56   0.03   0.58   2.37 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 1e+02    10.9          
##            day         4e-02     0.2     -1.00
##  Residual              1e+03    34.2          
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                              Estimate Std. Error    df t value Pr(>|t|)    
## (Intercept)                     244.2       13.9  23.9      18    3e-15 ***
## day                              -7.3        0.6  43.0     -11    2e-14 ***
## patch_size_symmetrysymmetric     17.0       10.0  10.3       2      0.1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.860       
## ptch_sz_sym -0.361  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.4   0.122     none
results_table = fill.results.table(
  results_table,
  response_variable,
  metaecosystem_type_i,
  model_stats_full,
  model_stats_fixed
)
SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
                         "Small-Large isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -7.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.5e-04
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -7.0e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.3e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -8.8e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.9e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -8.7e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -8.8e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -8.5e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -8.3e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.6e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.5   0.105     none
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##     system_nr_isolated_systems   ΔAIC_full
## 1     1004 1010 1012 1016 1023 -0.92627989
## 2     1001 1010 1013 1019 1022 -0.92194721
## 3     1002 1010 1011 1019 1023 -0.89185553
## 4     1001 1010 1012 1019 1023 -0.88284718
## 5     1004 1010 1011 1017 1023 -0.87435072
## 6     1001 1010 1014 1017 1023 -0.84078618
## 7     1002 1010 1014 1016 1023 -0.84063425
## 8     1004 1010 1011 1018 1022 -0.82425333
## 9     1002 1008 1014 1016 1025 -0.81977787
## 10    1002 1008 1011 1019 1025 -0.81768418
## 11    1001 1008 1014 1017 1025 -0.81140017
## 12    1001 1008 1012 1019 1025 -0.80370441
## 13    1004 1010 1012 1018 1021 -0.79621450
## 14    1004 1008 1011 1017 1025 -0.79244497
## 15    1004 1008 1012 1016 1025 -0.78410015
## 16    1004 1007 1013 1016 1025 -0.77541657
## 17    1002 1010 1013 1019 1021 -0.76954554
## 18    1004 1010 1013 1017 1021 -0.76313103
## 19    1004 1010 1013 1016 1022 -0.74894995
## 20    1002 1006 1013 1019 1025 -0.74485229
## 21    1003 1010 1012 1019 1021 -0.73502495
## 22    1003 1010 1011 1019 1022 -0.72651017
## 23    1002 1010 1014 1018 1021 -0.69591543
## 24    1003 1010 1014 1017 1021 -0.69505526
## 25    1001 1010 1014 1018 1022 -0.68099063
## 26    1001 1007 1014 1020 1023 -0.68067240
## 27    1003 1010 1014 1016 1022 -0.67967352
## 28    1001 1007 1015 1019 1023 -0.67772504
## 29    1004 1006 1013 1017 1025 -0.66379182
## 30    1004 1006 1012 1018 1025 -0.64501046
## 31    1005 1007 1014 1016 1023 -0.64333629
## 32    1001 1007 1013 1019 1025 -0.64002588
## 33    1003 1006 1012 1019 1025 -0.63557902
## 34    1004 1007 1011 1018 1025 -0.63088882
## 35    1002 1008 1015 1019 1021 -0.62702321
## 36    1002 1006 1015 1019 1023 -0.62646971
## 37    1005 1007 1011 1019 1023 -0.62391197
## 38    1001 1008 1015 1019 1022 -0.62293486
## 39    1003 1007 1011 1019 1025 -0.61984431
## 40    1002 1006 1014 1018 1025 -0.60906854
## 41    1004 1008 1015 1016 1022 -0.60592069
## 42    1001 1010 1012 1018 1024 -0.60590850
## 43    1003 1006 1014 1017 1025 -0.60439690
## 44    1004 1008 1015 1017 1021 -0.59406116
## 45    1001 1007 1014 1018 1025 -0.59338163
## 46    1003 1007 1014 1016 1025 -0.58832473
## 47    1005 1008 1014 1016 1022 -0.58579153
## 48    1005 1008 1011 1019 1022 -0.58548095
## 49    1005 1006 1014 1017 1023 -0.58496490
## 50    1004 1006 1015 1017 1023 -0.58426010
## 51    1001 1009 1013 1017 1025 -0.58343834
## 52    1005 1007 1014 1018 1021 -0.58254171
## 53    1003 1010 1011 1017 1024 -0.57912974
## 54    1004 1007 1015 1016 1023 -0.57899168
## 55    1004 1008 1011 1020 1022 -0.57788109
## 56    1005 1008 1014 1017 1021 -0.57389144
## 57    1003 1010 1012 1016 1024 -0.56819806
## 58    1005 1008 1012 1019 1021 -0.56324710
## 59    1004 1008 1012 1020 1021 -0.56052857
## 60    1005 1007 1013 1019 1021 -0.55707196
## 61    1002 1009 1013 1016 1025 -0.55634299
## 62    1004 1007 1011 1020 1023 -0.55496756
## 63    1003 1007 1015 1019 1021 -0.54985950
## 64    1005 1006 1012 1019 1023 -0.54923920
## 65    1002 1006 1014 1020 1023 -0.53388522
## 66    1001 1009 1013 1020 1022 -0.51570048
## 67    1005 1006 1013 1019 1022 -0.51091045
## 68    1002 1009 1013 1020 1021 -0.51038212
## 69    1003 1006 1015 1019 1022 -0.50953169
## 70    1002 1010 1013 1016 1024 -0.49051453
## 71    1001 1010 1013 1017 1024 -0.49047582
## 72    1001 1009 1015 1017 1023 -0.48313254
## 73    1002 1009 1015 1016 1023 -0.48286001
## 74    1002 1010 1011 1018 1024 -0.47146211
## 75    1004 1006 1015 1018 1022 -0.46582286
## 76    1004 1007 1015 1018 1021 -0.46202535
## 77    1001 1008 1014 1020 1022 -0.46179867
## 78    1001 1009 1015 1018 1022 -0.46055774
## 79    1004 1006 1013 1020 1022 -0.45784165
## 80    1004 1007 1013 1020 1021 -0.45462893
## 81    1005 1009 1013 1016 1022 -0.45427478
## 82    1002 1008 1014 1020 1021 -0.45341845
## 83    1005 1009 1013 1017 1021 -0.45029358
## 84    1003 1007 1011 1020 1024 -0.44064689
## 85    1002 1008 1015 1016 1024 -0.43341035
## 86    1001 1008 1015 1017 1024 -0.42867661
## 87    1004 1006 1012 1020 1023 -0.40742238
## 88    1003 1007 1014 1020 1021 -0.40688031
## 89    1005 1006 1014 1018 1022 -0.40575107
## 90    1003 1006 1014 1020 1022 -0.40050484
## 91    1002 1006 1013 1020 1024 -0.39965899
## 92    1002 1006 1015 1018 1024 -0.37661727
## 93    1002 1008 1011 1020 1024 -0.36814997
## 94    1003 1006 1012 1020 1024 -0.36224010
## 95    1005 1006 1013 1017 1024 -0.35972735
## 96    1003 1006 1015 1017 1024 -0.34952617
## 97    1001 1008 1012 1020 1024 -0.34155563
## 98    1002 1009 1011 1018 1025 -0.33045400
## 99    1005 1008 1011 1017 1024 -0.32708879
## 100   1003 1009 1011 1017 1025 -0.32117455
## 101   1003 1009 1012 1016 1025 -0.31198903
## 102   1001 1009 1012 1018 1025 -0.30792392
## 103   1005 1008 1012 1016 1024 -0.30362170
## 104   1001 1009 1012 1020 1023 -0.26376699
## 105   1002 1009 1011 1020 1023 -0.26186607
## 106   1005 1009 1012 1016 1023 -0.25049849
## 107   1005 1009 1011 1017 1023 -0.25047788
## 108   1003 1007 1015 1016 1024 -0.22664344
## 109   1005 1007 1013 1016 1024 -0.21734352
## 110   1005 1006 1012 1018 1024 -0.21288844
## 111   1003 1009 1015 1017 1021 -0.19180395
## 112   1002 1009 1015 1018 1021 -0.17119465
## 113   1003 1009 1012 1020 1021 -0.16699839
## 114   1005 1009 1012 1018 1021 -0.16241706
## 115   1003 1009 1015 1016 1022 -0.15771439
## 116   1003 1009 1011 1020 1022 -0.15173440
## 117   1005 1007 1011 1018 1024 -0.14928012
## 118   1005 1009 1011 1018 1022 -0.14298440
## 119   1001 1007 1013 1020 1024 -0.10287060
## 120   1001 1007 1015 1018 1024 -0.07807924

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.7   0.264     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.2   0.078   * weak
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##    system_nr_isolated_systems   ΔAIC_full
## 1                   1051 1054 -1.92651033
## 2                   1054 1051 -1.92651033
## 3                   1050 1055 -1.84623870
## 4                   1055 1050 -1.84623870
## 5                   1052 1053 -1.74248955
## 6                   1053 1052 -1.74248955
## 7                   1047 1051 -1.64874878
## 8                   1051 1047 -1.64874878
## 9                   1047 1055 -1.57909579
## 10                  1055 1047 -1.57909579
## 11                  1047 1052 -1.45439502
## 12                  1052 1047 -1.45439502
## 13                  1048 1050 -1.43231868
## 14                  1050 1048 -1.43231868
## 15                  1048 1054 -1.41715448
## 16                  1054 1048 -1.41715448
## 17                  1046 1053 -1.32907802
## 18                  1053 1046 -1.32907802
## 19                  1046 1054 -1.29569680
## 20                  1054 1046 -1.29569680
## 21                  1049 1053 -1.21633078
## 22                  1053 1049 -1.21633078
## 23                  1049 1050 -1.19859008
## 24                  1050 1049 -1.19859008
## 25                  1048 1052 -0.04237190
## 26                  1052 1048 -0.04237190
## 27                  1046 1055 -0.03078654
## 28                  1055 1046 -0.03078654
## 29                  1049 1051  0.02308620
## 30                  1051 1049  0.02308620

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1     -3.1   0.026 ** moderate
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )

Alpha (mean Shannon)

response_variable = "mean_shannon"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Small-Large meta-ecosystem")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_symmetry and patch_size_symmetry : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry +
    patch_size_symmetry : day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: 
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +  
##     (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -12.6      4.2     14.3    -28.6       52 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.4   -0.6    0.1    0.7    1.9 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 9e-03    0.092         
##            day         3e-06    0.002    -1.00
##  Residual              3e-02    0.183         
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                                  Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                         1.340      0.103 19.481    13.0    4e-11
## day                                 0.003      0.005 39.884     0.7      0.5
## patch_size_symmetrysymmetric        0.022      0.145 19.481     0.1      0.9
## day:patch_size_symmetrysymmetric   -0.005      0.007 39.884    -0.7      0.5
##                                     
## (Intercept)                      ***
## day                                 
## patch_size_symmetrysymmetric        
## day:patch_size_symmetrysymmetric    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.910              
## ptch_sz_sym -0.707  0.644       
## dy:ptch_sz_  0.644 -0.707 -0.910
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.5   0.464     none

 
 
 

Reduced model (with patch_size_symmetry)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -14.2      0.5     14.1    -28.2       53 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.5   -0.6    0.1    0.7    1.9 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 9e-03    0.094         
##            day         4e-06    0.002    -1.00
##  Residual              3e-02    0.183         
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                              Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)                     1.383      0.079 21.073    17.5    5e-14 ***
## day                             0.001      0.004 38.497     0.3      0.7    
## patch_size_symmetrysymmetric   -0.065      0.060 10.623    -1.1      0.3    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.843       
## ptch_sz_sym -0.381  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.9   0.293     none
results_table = fill.results.table(
  results_table,
  response_variable,
  metaecosystem_type_i,
  model_stats_full,
  model_stats_fixed
)
SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
                         "Small-Large isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.6e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.8e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.9e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.8e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.9e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.5e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+03
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -8.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1     -9.4   0.002 *** strong
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##     system_nr_isolated_systems  ΔAIC_full
## 1     1004 1008 1015 1016 1022 -19.249457
## 2     1005 1007 1011 1019 1023 -17.020063
## 3     1005 1007 1013 1019 1021 -16.984911
## 4     1005 1006 1013 1017 1024 -15.299917
## 5     1001 1010 1014 1018 1022 -13.462412
## 6     1001 1007 1014 1018 1025 -13.461151
## 7     1003 1010 1014 1016 1022 -13.105110
## 8     1001 1009 1012 1018 1025 -13.030802
## 9     1003 1007 1014 1016 1025 -13.028598
## 10    1001 1010 1012 1018 1024 -12.983783
## 11    1004 1007 1011 1018 1025 -12.872192
## 12    1004 1010 1011 1018 1022 -12.792253
## 13    1003 1010 1012 1016 1024 -12.672256
## 14    1003 1009 1012 1016 1025 -12.520275
## 15    1002 1010 1011 1018 1024 -12.453324
## 16    1001 1009 1015 1018 1022 -12.332001
## 17    1005 1009 1011 1018 1022 -12.330034
## 18    1002 1009 1011 1018 1025 -12.283299
## 19    1004 1007 1013 1016 1025 -11.957878
## 20    1003 1009 1015 1016 1022 -11.918491
## 21    1004 1010 1013 1016 1022 -11.898049
## 22    1001 1007 1015 1018 1024 -11.850347
## 23    1005 1007 1011 1018 1024 -11.647281
## 24    1003 1007 1015 1016 1024 -11.568210
## 25    1002 1010 1013 1016 1024 -11.529644
## 26    1005 1009 1013 1016 1022 -11.348359
## 27    1003 1007 1011 1019 1025 -11.346753
## 28    1002 1009 1013 1016 1025 -11.341393
## 29    1003 1010 1011 1019 1022 -11.255111
## 30    1001 1007 1013 1019 1025 -11.003809
## 31    1001 1010 1013 1019 1022 -10.920166
## 32    1005 1007 1013 1016 1024 -10.854462
## 33    1004 1006 1012 1018 1025 -10.652597
## 34    1002 1010 1014 1018 1021 -10.519464
## 35    1004 1010 1012 1018 1021 -10.516555
## 36    1002 1006 1014 1018 1025 -10.447518
## 37    1003 1010 1011 1017 1024 -10.413239
## 38    1005 1009 1012 1018 1021 -10.351110
## 39    1003 1009 1011 1017 1025 -10.272903
## 40    1001 1010 1013 1017 1024 -10.042969
## 41    1005 1006 1014 1018 1022 -10.011042
## 42    1005 1006 1012 1018 1024 -10.008257
## 43    1002 1009 1015 1018 1021  -9.985341
## 44    1001 1009 1013 1017 1025  -9.981875
## 45    1005 1007 1014 1018 1021  -9.952512
## 46    1003 1009 1011 1020 1022  -9.892339
## 47    1004 1006 1015 1018 1022  -9.842749
## 48    1003 1006 1012 1019 1025  -9.807286
## 49    1004 1007 1015 1018 1021  -9.786107
## 50    1002 1006 1015 1018 1024  -9.708343
## 51    1003 1007 1011 1020 1024  -9.642038
## 52    1001 1009 1013 1020 1022  -9.469370
## 53    1004 1008 1012 1016 1025  -9.394285
## 54    1003 1010 1012 1019 1021  -9.329551
## 55    1001 1007 1013 1020 1024  -9.234592
## 56    1003 1006 1014 1017 1025  -9.153448
## 57    1001 1008 1012 1019 1025  -9.111903
## 58    1004 1010 1012 1016 1023  -9.082423
## 59    1002 1010 1014 1016 1023  -8.975994
## 60    1003 1010 1014 1017 1021  -8.856601
## 61    1003 1006 1015 1019 1022  -8.741857
## 62    1003 1007 1015 1019 1021  -8.593798
## 63    1005 1009 1012 1016 1023  -8.589662
## 64    1001 1008 1014 1017 1025  -8.497198
## 65    1004 1007 1015 1016 1023  -8.478950
## 66    1005 1007 1014 1016 1023  -8.456976
## 67    1005 1008 1014 1016 1022  -8.446301
## 68    1004 1006 1013 1017 1025  -8.440749
## 69    1002 1010 1013 1019 1021  -8.426875
## 70    1002 1008 1011 1019 1025  -8.411446
## 71    1005 1008 1012 1016 1024  -8.401542
## 72    1002 1009 1015 1016 1023  -8.384876
## 73    1002 1008 1015 1016 1024  -8.267571
## 74    1002 1008 1014 1016 1025  -8.251191
## 75    1004 1010 1013 1017 1021  -8.214667
## 76    1003 1009 1012 1020 1021  -8.203788
## 77    1003 1009 1015 1017 1021  -8.137319
## 78    1001 1008 1015 1019 1022  -8.114672
## 79    1003 1006 1015 1017 1024  -8.093688
## 80    1001 1010 1014 1017 1023  -8.087881
## 81    1004 1008 1011 1017 1025  -8.079184
## 82    1003 1007 1014 1020 1021  -8.061842
## 83    1003 1006 1014 1020 1022  -7.963107
## 84    1002 1010 1011 1019 1023  -7.921859
## 85    1001 1007 1015 1019 1023  -7.854816
## 86    1002 1009 1013 1020 1021  -7.848309
## 87    1005 1009 1013 1017 1021  -7.830720
## 88    1005 1006 1013 1019 1022  -7.795816
## 89    1003 1006 1012 1020 1024  -7.750362
## 90    1004 1010 1011 1017 1023  -7.711369
## 91    1002 1006 1013 1020 1024  -7.508292
## 92    1004 1007 1013 1020 1021  -7.439012
## 93    1001 1010 1012 1019 1023  -7.377810
## 94    1004 1006 1013 1020 1022  -7.377590
## 95    1001 1008 1015 1017 1024  -7.351056
## 96    1001 1009 1015 1017 1023  -7.339624
## 97    1005 1008 1011 1019 1022  -7.280906
## 98    1001 1009 1012 1020 1023  -7.229004
## 99    1001 1007 1014 1020 1023  -7.224164
## 100   1001 1008 1014 1020 1022  -7.195944
## 101   1005 1009 1011 1017 1023  -7.146015
## 102   1002 1009 1011 1020 1023  -7.108867
## 103   1001 1008 1012 1020 1024  -7.093905
## 104   1005 1008 1011 1017 1024  -6.980639
## 105   1002 1006 1013 1019 1025  -6.917538
## 106   1004 1007 1011 1020 1023  -6.894940
## 107   1002 1008 1015 1019 1021  -6.479077
## 108   1004 1008 1015 1017 1021  -6.291151
## 109   1004 1006 1015 1017 1023  -6.240906
## 110   1005 1008 1012 1019 1021  -6.226379
## 111   1005 1006 1012 1019 1023  -6.197888
## 112   1005 1008 1014 1017 1021  -6.018750
## 113   1002 1008 1011 1020 1024  -5.888738
## 114   1004 1008 1012 1020 1021  -5.863729
## 115   1004 1006 1012 1020 1023  -5.856217
## 116   1002 1006 1014 1020 1023  -5.843640
## 117   1002 1008 1014 1020 1021  -5.833166
## 118   1004 1008 1011 1020 1022  -4.867119
## 119   1005 1006 1014 1017 1023  -4.268147
## 120   1002 1006 1015 1019 1023  -2.912301

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.483     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.9   0.591     none
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##    system_nr_isolated_systems ΔAIC_full
## 1                   1048 1054  2.250274
## 2                   1054 1048  2.250274
## 3                   1048 1050  2.252695
## 4                   1050 1048  2.252695
## 5                   1046 1053  2.329635
## 6                   1053 1046  2.329635
## 7                   1047 1051  2.383022
## 8                   1051 1047  2.383022
## 9                   1049 1053  2.417776
## 10                  1053 1049  2.417776
## 11                  1047 1055  2.531994
## 12                  1055 1047  2.531994
## 13                  1046 1054  2.677725
## 14                  1054 1046  2.677725
## 15                  1049 1050  2.768293
## 16                  1050 1049  2.768293
## 17                  1047 1052  2.932105
## 18                  1052 1047  2.932105
## 19                  1051 1054  3.103515
## 20                  1054 1051  3.103515
## 21                  1050 1055  3.167263
## 22                  1055 1050  3.167263
## 23                  1052 1053  3.237364
## 24                  1053 1052  3.237364
## 25                  1049 1051  3.697185
## 26                  1051 1049  3.697185
## 27                  1046 1055  3.706302
## 28                  1055 1046  3.706302
## 29                  1048 1052  3.711991
## 30                  1052 1048  3.711991

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.788     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )

Beta (Bray-Curtis)

response_variable = "bray_curtis"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Small-Large meta-ecosystem")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_symmetry and patch_size_symmetry : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry +
    patch_size_symmetry : day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: 
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +  
##     (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -68.4    -51.6     42.2    -84.4       52 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.33  -0.58  -0.03   0.63   2.01 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 3e-13    5e-07         
##            day         9e-16    3e-08    -1.00
##  Residual              1e-02    1e-01         
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                                  Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                         0.360      0.062 60.000     5.8    2e-07
## day                                 0.007      0.003 60.000     2.1     0.04
## patch_size_symmetrysymmetric        0.047      0.087 60.000     0.5     0.59
## day:patch_size_symmetrysymmetric   -0.006      0.005 60.000    -1.3     0.21
##                                     
## (Intercept)                      ***
## day                              *  
## patch_size_symmetrysymmetric        
## day:patch_size_symmetrysymmetric    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.935              
## ptch_sz_sym -0.707  0.661       
## dy:ptch_sz_  0.661 -0.707 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.6   0.099   * weak

 
 
 

Reduced model (with patch_size_symmetry)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -68.8    -54.1     41.4    -82.8       53 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.54  -0.67  -0.04   0.66   1.93 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 6e-13    8e-07         
##            day         1e-15    3e-08    -1.00
##  Residual              1e-02    1e-01         
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                              Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)                     0.411      0.047 60.000       9    2e-12 ***
## day                             0.004      0.002 60.000       2     0.10 .  
## patch_size_symmetrysymmetric   -0.056      0.031 60.000      -2     0.08 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.881       
## ptch_sz_sym -0.334  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.1    0.08   * weak
results_table = fill.results.table(
  results_table,
  response_variable,
  metaecosystem_type_i,
  model_stats_full,
  model_stats_fixed
)
SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
                         "Small-Large isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.7e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e-02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.5e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1    -12.4   0.007 *** strong
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##     system_nr_isolated_systems   ΔAIC_full
## 1     1003 1007 1015 1016 1024 -23.6032253
## 2     1002 1008 1015 1016 1024 -22.3439447
## 3     1005 1008 1012 1016 1024 -22.2510431
## 4     1005 1008 1011 1019 1022 -21.2082743
## 5     1005 1006 1013 1019 1022 -20.6450651
## 6     1003 1010 1011 1019 1022 -19.9619334
## 7     1001 1007 1013 1019 1025 -19.7922111
## 8     1002 1006 1013 1019 1025 -19.4185919
## 9     1005 1007 1011 1018 1024 -19.3463546
## 10    1003 1007 1011 1020 1024 -19.1386767
## 11    1002 1008 1015 1019 1021 -19.0089440
## 12    1002 1008 1011 1020 1024 -18.3564894
## 13    1002 1010 1011 1018 1024 -18.3193147
## 14    1005 1008 1011 1017 1024 -18.2289310
## 15    1005 1007 1013 1016 1024 -18.1285187
## 16    1003 1010 1011 1017 1024 -17.9028991
## 17    1003 1010 1012 1019 1021 -17.7820316
## 18    1001 1007 1013 1020 1024 -17.5583143
## 19    1002 1006 1013 1020 1024 -17.4044416
## 20    1001 1007 1015 1018 1024 -17.3703409
## 21    1002 1006 1015 1018 1024 -17.2704297
## 22    1005 1006 1013 1017 1024 -17.2440605
## 23    1002 1010 1013 1016 1024 -17.1188911
## 24    1005 1006 1012 1018 1024 -17.1174820
## 25    1003 1006 1012 1020 1024 -17.0081684
## 26    1002 1009 1015 1018 1021 -16.9812430
## 27    1003 1006 1015 1017 1024 -16.9347849
## 28    1004 1006 1015 1018 1022 -16.8607485
## 29    1003 1010 1012 1016 1024 -16.8123348
## 30    1002 1009 1015 1016 1023 -16.7612477
## 31    1005 1009 1012 1018 1021 -16.6485319
## 32    1001 1010 1013 1017 1024 -16.5039406
## 33    1001 1008 1012 1020 1024 -16.4769424
## 34    1001 1010 1012 1018 1024 -16.4289791
## 35    1001 1008 1015 1017 1024 -16.4088006
## 36    1004 1007 1013 1016 1025 -16.3410232
## 37    1001 1009 1015 1017 1023 -15.9373692
## 38    1005 1006 1014 1018 1022 -15.2619106
## 39    1003 1007 1014 1016 1025 -14.8284133
## 40    1003 1010 1014 1016 1022 -14.5355625
## 41    1001 1008 1014 1020 1022 -14.5155532
## 42    1001 1007 1014 1018 1025 -14.4583717
## 43    1003 1007 1011 1019 1025 -14.1882582
## 44    1005 1007 1013 1019 1021 -14.1263198
## 45    1004 1010 1012 1016 1023 -14.0523185
## 46    1003 1007 1015 1019 1021 -13.9325971
## 47    1005 1007 1014 1018 1021 -13.8480044
## 48    1002 1008 1011 1019 1025 -13.8315706
## 49    1002 1010 1013 1019 1021 -13.8167285
## 50    1001 1007 1014 1020 1023 -13.5917284
## 51    1005 1007 1011 1019 1023 -13.3398966
## 52    1003 1006 1015 1019 1022 -13.2539201
## 53    1005 1008 1012 1019 1021 -13.2038251
## 54    1003 1006 1012 1019 1025 -13.1804566
## 55    1002 1010 1011 1019 1023 -13.0049436
## 56    1001 1010 1013 1019 1022 -12.8601572
## 57    1001 1008 1015 1019 1022 -12.5344105
## 58    1005 1009 1011 1018 1022 -12.4779641
## 59    1002 1009 1011 1018 1025 -12.4220539
## 60    1003 1009 1011 1020 1022 -12.4110411
## 61    1002 1009 1013 1020 1021 -12.4007121
## 62    1001 1008 1012 1019 1025 -12.3920125
## 63    1002 1006 1015 1019 1023 -12.3894220
## 64    1003 1009 1011 1017 1025 -12.2646293
## 65    1005 1009 1013 1017 1021 -12.1939510
## 66    1005 1006 1012 1019 1023 -12.0869860
## 67    1003 1009 1012 1020 1021 -11.9974231
## 68    1003 1009 1015 1017 1021 -11.9857526
## 69    1001 1007 1015 1019 1023 -11.9330563
## 70    1002 1009 1013 1016 1025 -11.7566153
## 71    1002 1009 1011 1020 1023 -11.6341627
## 72    1005 1009 1013 1016 1022 -11.5897457
## 73    1001 1010 1012 1019 1023 -11.5394740
## 74    1003 1009 1012 1016 1025 -11.5007515
## 75    1001 1009 1013 1020 1022 -11.4343658
## 76    1005 1009 1011 1017 1023 -11.3995681
## 77    1003 1009 1015 1016 1022 -11.3951793
## 78    1001 1009 1013 1017 1025 -11.3487519
## 79    1001 1009 1015 1018 1022 -11.1684533
## 80    1001 1009 1012 1018 1025 -11.1293018
## 81    1005 1009 1012 1016 1023 -10.4671771
## 82    1001 1009 1012 1020 1023 -10.3425093
## 83    1003 1007 1014 1020 1021  -9.8142296
## 84    1003 1006 1014 1020 1022  -9.7527196
## 85    1002 1006 1014 1018 1025  -9.5720835
## 86    1002 1010 1014 1018 1021  -9.5506955
## 87    1002 1008 1014 1020 1021  -9.5111077
## 88    1003 1006 1014 1017 1025  -9.4071616
## 89    1002 1008 1014 1016 1025  -9.3819928
## 90    1005 1008 1014 1016 1022  -9.3195634
## 91    1003 1010 1014 1017 1021  -9.3064732
## 92    1001 1010 1014 1018 1022  -9.2439662
## 93    1005 1008 1014 1017 1021  -9.2187726
## 94    1002 1006 1014 1020 1023  -8.9893260
## 95    1001 1008 1014 1017 1025  -8.8180065
## 96    1005 1007 1014 1016 1023  -8.8136450
## 97    1002 1010 1014 1016 1023  -8.7412800
## 98    1005 1006 1014 1017 1023  -8.6489524
## 99    1001 1010 1014 1017 1023  -8.2893284
## 100   1004 1010 1011 1018 1022  -5.6962742
## 101   1004 1007 1011 1018 1025  -5.6855647
## 102   1004 1008 1011 1020 1022  -5.6382083
## 103   1004 1007 1011 1020 1023  -5.4994234
## 104   1004 1008 1011 1017 1025  -5.2449966
## 105   1004 1007 1013 1020 1021  -5.1384486
## 106   1004 1010 1011 1017 1023  -5.1268641
## 107   1004 1007 1015 1018 1021  -4.8776450
## 108   1004 1010 1013 1017 1021  -4.8303304
## 109   1004 1010 1013 1016 1022  -4.6567662
## 110   1004 1008 1015 1017 1021  -4.5459723
## 111   1004 1010 1012 1018 1021  -4.5143183
## 112   1004 1008 1012 1020 1021  -4.4532780
## 113   1004 1008 1015 1016 1022  -4.3798257
## 114   1004 1006 1012 1018 1025  -4.3630675
## 115   1004 1007 1015 1016 1023  -4.1999584
## 116   1004 1006 1012 1020 1023  -4.1700004
## 117   1004 1008 1012 1016 1025  -4.0607610
## 118   1004 1006 1013 1017 1025   0.5317163
## 119   1004 1006 1013 1020 1022   0.8298978
## 120   1004 1006 1015 1017 1023   1.1434381

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -3.6   0.075   * weak
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
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## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
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## 1e-15, : unused control arguments ignored
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## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.8   0.308     none
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##    system_nr_isolated_systems  ΔAIC_full
## 1                   1047 1052 -2.9518507
## 2                   1052 1047 -2.9518507
## 3                   1050 1055 -2.1310860
## 4                   1055 1050 -2.1310860
## 5                   1047 1051 -2.0992457
## 6                   1051 1047 -2.0992457
## 7                   1047 1055 -1.6448276
## 8                   1055 1047 -1.6448276
## 9                   1052 1053 -1.3285235
## 10                  1053 1052 -1.3285235
## 11                  1046 1055  0.3823059
## 12                  1055 1046  0.3823059
## 13                  1048 1050  1.6910361
## 14                  1050 1048  1.6910361
## 15                  1046 1053  1.8190972
## 16                  1053 1046  1.8190972
## 17                  1049 1051  1.9617634
## 18                  1051 1049  1.9617634
## 19                  1051 1054  1.9863126
## 20                  1054 1051  1.9863126
## 21                  1049 1053  2.0137185
## 22                  1053 1049  2.0137185
## 23                  1049 1050  2.0657045
## 24                  1050 1049  2.0657045
## 25                  1048 1052  2.5035401
## 26                  1052 1048  2.5035401
## 27                  1046 1054  3.1677620
## 28                  1054 1046  3.1677620
## 29                  1048 1054  3.8923779
## 30                  1054 1048  3.8923779

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.5   0.236     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )

Gamma (regional richness)

response_variable = "metaecosystem_richness"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")
plot.metaecos.points(ds_metaecosystems,
                     metaecosystem_type_i,
                     response_variable,
                     3)

Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
                       metaecosystem_type_i,
                       response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
                         metaecosystem_type_i,
                         response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Small-Large meta-ecosystem")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_symmetry and patch_size_symmetry : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry +
    patch_size_symmetry : day + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: 
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +  
##     (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    206.3    223.1    -95.2    190.3       52 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.4   -0.6    0.1    0.7    1.6 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 0.053    0.23          
##            day         0.002    0.04     -1.00
##  Residual              1.181    1.09          
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                                  Estimate Std. Error    df t value Pr(>|t|)    
## (Intercept)                          8.08       0.57 38.23    14.2   <2e-16 ***
## day                                 -0.01       0.03 15.06    -0.3      0.8    
## patch_size_symmetrysymmetric         0.46       0.80 38.23     0.6      0.6    
## day:patch_size_symmetrysymmetric    -0.01       0.05 15.06    -0.3      0.8    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.874              
## ptch_sz_sym -0.707  0.618       
## dy:ptch_sz_  0.618 -0.707 -0.874
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.4   0.752     none

 
 
 

Reduced model (with patch_size_symmetry)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_size_symmetry + 
    (day | system_nr), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    204.4    219.0    -95.2    190.4       53 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.4   -0.6    0.1    0.7    1.6 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev. Corr 
##  system_nr (Intercept) 0.055    0.24          
##            day         0.002    0.04     -1.00
##  Residual              1.182    1.09          
## Number of obs: 60, groups:  system_nr, 10
## 
## Fixed effects:
##                              Estimate Std. Error    df t value Pr(>|t|)    
## (Intercept)                      8.17       0.45 55.73    18.3   <2e-16 ***
## day                             -0.02       0.02 14.96    -0.7      0.5    
## patch_size_symmetrysymmetric     0.28       0.39 13.55     0.7      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.786       
## ptch_sz_sym -0.437  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$system_nr, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.5   0.479     none
results_table = fill.results.table(
  results_table,
  response_variable,
  metaecosystem_type_i,
  model_stats_full,
  model_stats_fixed
)
SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
                         "Small-Large isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -8.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -5.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.3e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -7.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.2   0.432     none
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##     system_nr_isolated_systems ΔAIC_full
## 1     1002 1009 1013 1016 1025 0.5010946
## 2     1002 1010 1013 1016 1024 0.5570386
## 3     1003 1007 1014 1016 1025 0.6254584
## 4     1005 1007 1013 1016 1024 0.9638950
## 5     1002 1009 1013 1020 1021 0.9759178
## 6     1003 1007 1014 1020 1021 1.1759537
## 7     1002 1006 1013 1020 1024 1.2012104
## 8     1003 1010 1014 1016 1022 1.2401001
## 9     1002 1006 1013 1019 1025 1.2794401
## 10    1002 1010 1013 1019 1021 1.3088651
## 11    1002 1008 1014 1016 1025 1.3159753
## 12    1005 1007 1013 1019 1021 1.3701184
## 13    1005 1009 1013 1016 1022 1.4030246
## 14    1004 1007 1013 1016 1025 1.4352352
## 15    1005 1007 1014 1018 1021 1.4939106
## 16    1002 1010 1014 1018 1021 1.4959565
## 17    1003 1007 1011 1019 1025 1.5059323
## 18    1002 1009 1011 1018 1025 1.5389815
## 19    1002 1006 1014 1018 1025 1.5660723
## 20    1002 1010 1011 1018 1024 1.5673278
## 21    1003 1007 1011 1020 1024 1.5689721
## 22    1001 1007 1013 1019 1025 1.6118521
## 23    1001 1007 1013 1020 1024 1.6227912
## 24    1003 1010 1014 1017 1021 1.6749332
## 25    1003 1006 1012 1020 1024 1.6976609
## 26    1005 1006 1012 1018 1024 1.7255034
## 27    1003 1006 1014 1020 1022 1.7425675
## 28    1003 1006 1014 1017 1025 1.7474833
## 29    1001 1007 1014 1018 1025 1.7954154
## 30    1002 1008 1014 1020 1021 1.7989294
## 31    1005 1008 1014 1016 1022 1.8140579
## 32    1002 1010 1014 1016 1023 1.8221258
## 33    1005 1007 1014 1016 1023 1.8243680
## 34    1004 1007 1013 1020 1021 1.8683582
## 35    1005 1007 1011 1018 1024 1.8980400
## 36    1002 1008 1011 1020 1024 1.9284091
## 37    1005 1009 1013 1017 1021 1.9322277
## 38    1005 1006 1013 1019 1022 1.9405194
## 39    1003 1009 1011 1020 1022 1.9433170
## 40    1003 1010 1011 1019 1022 1.9527565
## 41    1003 1007 1015 1016 1024 1.9637630
## 42    1002 1008 1011 1019 1025 1.9662347
## 43    1003 1010 1012 1016 1024 1.9788621
## 44    1004 1010 1013 1016 1022 1.9891498
## 45    1003 1009 1012 1016 1025 2.0038790
## 46    1005 1006 1014 1018 1022 2.0118944
## 47    1001 1009 1013 1020 1022 2.0178475
## 48    1003 1010 1011 1017 1024 2.0486805
## 49    1003 1009 1011 1017 1025 2.0667909
## 50    1005 1006 1013 1017 1024 2.0817697
## 51    1001 1010 1013 1019 1022 2.0829855
## 52    1005 1008 1012 1016 1024 2.0943513
## 53    1001 1009 1013 1017 1025 2.1275456
## 54    1002 1009 1011 1020 1023 2.1364686
## 55    1001 1010 1013 1017 1024 2.1377212
## 56    1001 1010 1012 1018 1024 2.1601263
## 57    1004 1007 1011 1018 1025 2.1666866
## 58    1005 1009 1011 1018 1022 2.2323686
## 59    1003 1007 1015 1019 1021 2.2363180
## 60    1001 1010 1014 1018 1022 2.2395147
## 61    1002 1010 1011 1019 1023 2.2437344
## 62    1005 1008 1014 1017 1021 2.2640440
## 63    1003 1009 1015 1016 1022 2.2769286
## 64    1003 1006 1012 1019 1025 2.2784983
## 65    1003 1010 1012 1019 1021 2.2792302
## 66    1002 1009 1015 1018 1021 2.3107020
## 67    1002 1006 1014 1020 1023 2.3319116
## 68    1002 1008 1015 1016 1024 2.3450472
## 69    1004 1006 1013 1020 1022 2.3626482
## 70    1003 1009 1012 1020 1021 2.3728213
## 71    1004 1010 1013 1017 1021 2.4161909
## 72    1005 1007 1011 1019 1023 2.4303297
## 73    1004 1006 1013 1017 1025 2.4403362
## 74    1001 1007 1014 1020 1023 2.4594287
## 75    1002 1006 1015 1018 1024 2.4842527
## 76    1001 1008 1014 1020 1022 2.4992415
## 77    1002 1009 1015 1016 1023 2.5079605
## 78    1005 1008 1011 1019 1022 2.5093976
## 79    1001 1008 1014 1017 1025 2.5321129
## 80    1005 1009 1012 1018 1021 2.5754843
## 81    1004 1010 1011 1018 1022 2.6099980
## 82    1003 1006 1015 1019 1022 2.6432113
## 83    1005 1008 1011 1017 1024 2.6492482
## 84    1002 1008 1015 1019 1021 2.6501305
## 85    1001 1008 1012 1020 1024 2.6743575
## 86    1005 1006 1014 1017 1023 2.6758220
## 87    1003 1009 1015 1017 1021 2.6876740
## 88    1001 1007 1015 1018 1024 2.6914272
## 89    1004 1007 1011 1020 1023 2.6995192
## 90    1004 1007 1015 1018 1021 2.7190559
## 91    1005 1008 1012 1019 1021 2.7298626
## 92    1005 1009 1012 1016 1023 2.7483390
## 93    1004 1008 1012 1016 1025 2.7655323
## 94    1003 1006 1015 1017 1024 2.7807335
## 95    1001 1009 1012 1018 1025 2.7868758
## 96    1004 1008 1011 1020 1022 2.7979655
## 97    1005 1009 1011 1017 1023 2.8289796
## 98    1004 1008 1011 1017 1025 2.8402370
## 99    1001 1010 1014 1017 1023 2.8671448
## 100   1004 1007 1015 1016 1023 2.8822776
## 101   1004 1006 1012 1018 1025 2.9060308
## 102   1004 1010 1012 1018 1021 2.9144904
## 103   1001 1009 1015 1018 1022 2.9359005
## 104   1001 1008 1012 1019 1025 2.9392128
## 105   1002 1006 1015 1019 1023 2.9487574
## 106   1004 1008 1015 1016 1022 2.9642506
## 107   1005 1006 1012 1019 1023 2.9961082
## 108   1004 1008 1012 1020 1021 3.0372131
## 109   1004 1010 1012 1016 1023 3.0627098
## 110   1004 1006 1015 1018 1022 3.0712925
## 111   1004 1010 1011 1017 1023 3.0801534
## 112   1001 1007 1015 1019 1023 3.0852168
## 113   1001 1010 1012 1019 1023 3.1583306
## 114   1001 1008 1015 1019 1022 3.1614639
## 115   1001 1009 1012 1020 1023 3.1837762
## 116   1001 1008 1015 1017 1024 3.2462909
## 117   1004 1008 1015 1017 1021 3.2565423
## 118   1004 1006 1012 1020 1023 3.2886902
## 119   1001 1009 1015 1017 1023 3.3609837
## 120   1004 1006 1015 1017 1023 3.4583155

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.8   0.737     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated")
filtered_data = ds_metaecosystems %>%
                         filter(time_point >= first_time_point_model,
                                time_point <= last_time_point_model,
                                metaecosystem_type %in% metaecosystem_type_i)
plot.metaecos.points(filtered_data,
                     metaecosystem_type_i,
                     response_variable)

The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.

Click for bootstrapping ΔAIC and p-value

 

If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.

# Compute stats for all patch combinations 

isolated_combinations_sets_filtered =  isolated_combinations_sets %>%
  filter(disturbance == disturbance_global_input,
         metaecosystem_type %in% metaecosystem_type_i)

n_sets = isolated_combinations_sets_filtered %>%
  pull(set) %>%
  max()

iterated_results_table = data.frame(
  Response = as.character(NA),
  Levels = as.character(NA),
  ΔAIC_full = NA,
  p_full = NA,
  ΔR2_full = NA,
  ΔAIC_fix = NA,
  p_fix = NA,
  ΔR2_fix = NA,
  combination_set = NA,
  system_nr_isolated_systems = as.character(NA)
) %>%
  slice(-1)

for (set_input in 1:n_sets) {
  
  system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
    filter(
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "isolated",
      set == set_input
    ) %>%
    pull(system_nr)
  
  filtered_data = ds_metaecosystems %>%
    filter(
      time_point >= first_time_point_model,
      time_point <= last_time_point_model,
      metaecosystem_type %in% metaecosystem_type_i,
      connection == "connected" |
        (connection == "isolated" &
           system_nr %in% system_nr_isolated_systems_input)
    )
  
  null_model = lmer(
    get(response_variable) ~
      day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  full_model = lmer(
    get(response_variable) ~
      day +
      connection +
      connection:day +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_full = compute.model.stats(full_model,
                                         null_model,
                                         "mixed_model")
  
  reduced_model = lmer(
    get(response_variable) ~
      day +
      connection +
      (day | system_nr),
    data = filtered_data,
    REML = FALSE,
    control = lmerControl(optimizer = optimizer_input,
                          optCtrl = list(method = method_input))
  )
  
  model_stats_fixed = compute.model.stats(reduced_model,
                                          null_model,
                                          "mixed_model")
  
  iterated_results_table = fill.results.table(
    iterated_results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )
  
  iterated_results_table$set[nrow(iterated_results_table)] = set_input
  iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e-01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+00
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored

 
 
 

Full model (with connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 

hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_full),
  p_value = mean(iterated_results_table$p_full),
  R2 = NA
)

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.3   0.282     none
Click for which system nr produced which AIC in the full model
iterated_results_table %>% 
  select(system_nr_isolated_systems,
         ΔAIC_full) %>%
  arrange(ΔAIC_full)
##    system_nr_isolated_systems   ΔAIC_full
## 1                   1052 1053 -0.11272141
## 2                   1053 1052 -0.11272141
## 3                   1048 1052 -0.09352802
## 4                   1052 1048 -0.09352802
## 5                   1047 1052  0.44975279
## 6                   1052 1047  0.44975279
## 7                   1048 1054  0.65487503
## 8                   1054 1048  0.65487503
## 9                   1047 1051  0.83275933
## 10                  1051 1047  0.83275933
## 11                  1047 1055  1.18971078
## 12                  1055 1047  1.18971078
## 13                  1048 1050  1.26756805
## 14                  1050 1048  1.26756805
## 15                  1049 1053  1.54078951
## 16                  1053 1049  1.54078951
## 17                  1051 1054  1.58837752
## 18                  1054 1051  1.58837752
## 19                  1046 1053  1.70905621
## 20                  1053 1046  1.70905621
## 21                  1050 1055  1.72028642
## 22                  1055 1050  1.72028642
## 23                  1049 1051  1.85003595
## 24                  1051 1049  1.85003595
## 25                  1046 1055  2.06900784
## 26                  1055 1046  2.06900784
## 27                  1046 1054  2.36391055
## 28                  1054 1046  2.36391055
## 29                  1049 1050  2.66198970
## 30                  1050 1049  2.66198970

 
 
 

Reduced model (with connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 

hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
  deltaAIC = mean(iterated_results_table$ΔAIC_fix),
  p_value = mean(iterated_results_table$p_fix),
  R2 = NA
)

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.4   0.242     none
results_table = fill.results.table(
    results_table,
    response_variable,
    metaecosystem_type_i,
    model_stats_full,
    model_stats_fixed
  )

All patches

patch_type_i = c("Small isolated",
                     "Medium isolated",
                     "Large isolated",
                     "Small connected to small",
                     "Small connected to large",
                     "Medium connected to medium",
                     "Large connected to small",
                     "Large connected to large")

BEF

Richness

ds_patches %>%
  filter(species_richness > 0,
         patch_type == patch_type_i) %>%
  ggplot(aes(x = species_richness,
             y = bioarea_mm2_per_ml)) +
  geom_point() +
  xlim(0, length(protist_species))

for (patch_type in patch_type_i) {
  plot.relationship.BEF(patch_type,
                        "species_richness") %>%
    print()
}

Shannon

ds_patches %>%
  filter(species_richness > 0,
         patch_type == patch_type_i) %>%
  ggplot(aes(x = shannon,
             y = bioarea_mm2_per_ml)) +
  geom_point()

for (patch_type in patch_type_i) {
  plot.relationship.BEF(patch_type,
                        "shannon") %>%
    print()
}

Evenness

ds_patches %>%
  filter(species_richness > 0,
         patch_type == patch_type_i) %>%
  ggplot(aes(x = evenness_pielou,
             y = bioarea_mm2_per_ml)) +
  geom_point() +
  xlim(0, 1)

for (patch_type in patch_type_i) {
  plot.relationship.BEF(patch_type,
                        "evenness_pielou") %>%
    print()
}

Pop dominance

for(species_input in protist_species_dominance){
  
  print(ds_patches %>%
  ggplot(aes(x = get(paste0(species_input)),
             y = bioarea_mm2_per_ml)) +
  geom_point() +
    labs(x = paste0(species_input, "_dominance")))
  
}
## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

## Warning: Removed 1 rows containing missing values (`geom_point()`).

for (patch_type in patch_type_i) {
  plot.relationship.dominance.productivity(patch_type,
                                         protist_species,
                                         protist_species_dominance) %>%
    print()
}

Isolated patches

patch_type_i = c("Small isolated",
                     "Medium isolated",
                     "Large isolated")

Biomass

response_variable = "bioarea_mm2_per_ml"
plot.patches.points(ds_patches,
                    patch_type_i,
                    response_variable)

Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
                       response_variable)
save_results_in_table = TRUE

 

Filter
filtered_data = ds_patches %>%
  filter(
    metaecosystem == "no",
    time_point >= first_time_point_model,
    time_point <= last_time_point_model,
    !is.na(!!sym(response_variable))
  )
Plot filtered data
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable)

Analysis
Null model
null_model = lmer(
  get(response_variable) ~
    day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_ml and patch_size_ml : day)
full_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    patch_size_ml : day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    251.0    271.0   -117.5    235.0       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.0   -0.6    0.0    0.6    2.8 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 2e-03    0.04         
##             day         1e-04    0.01     1.00
##  Residual               7e-01    0.86         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                   Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)          3.894      0.534 72.973       7    3e-10 ***
## day                 -0.183      0.028 39.417      -6    1e-07 ***
## patch_size_ml        0.034      0.021 72.973       2     0.11    
## day:patch_size_ml    0.002      0.001 39.417       2     0.07 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.900              
## patch_sz_ml -0.878  0.790       
## dy:ptch_sz_  0.790 -0.878 -0.900
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model") 

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -20.2 < 0.001 **** very strong

 
 
 

Reduced model (with patch_size_ml)
reduced_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    252.2    269.7   -119.1    238.2       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.0   -0.6    0.0    0.6    3.1 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 7e-02    0.26          
##             day         5e-04    0.02     -0.80
##  Residual               8e-01    0.87          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##               Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)      3.130      0.335 23.327       9    2e-09 ***
## day             -0.137      0.015 15.000      -9    1e-07 ***
## patch_size_ml    0.068      0.009 15.000       8    2e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.723       
## patch_sz_ml -0.606  0.000
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model") 

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1      -19 < 0.001 **** very strong
results_table = fill.results.table(results_table,
                                   response_variable,
                                   patch_type_i,
                                   model_stats_full,
                                   model_stats_fixed)

Shannon

response_variable = "shannon"
plot.patches.points(ds_patches,
                    patch_type_i,
                    response_variable)

Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
                       response_variable)
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE

 

Filter
filtered_data = ds_patches %>%
  filter(
    metaecosystem == "no",
    time_point >= first_time_point_model,
    time_point <= last_time_point_model,
    !is.na(!!sym(response_variable))
  )
Plot filtered data
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable)

Analysis
Null model
null_model = lmer(
  get(response_variable) ~
    day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_ml and patch_size_ml : day)
full_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    patch_size_ml : day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     65.7     85.6    -24.8     49.7       81 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.65  -0.63   0.03   0.72   2.46 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e-02    0.30          
##             day         4e-04    0.02     -1.00
##  Residual               9e-02    0.29          
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                   Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)          2e+00      2e-01  2e+01     6.3    7e-06 ***
## day                 -4e-02      1e-02  2e+01    -2.8     0.01 *  
## patch_size_ml        2e-03      1e-02  2e+01     0.2     0.88    
## day:patch_size_ml    1e-03      6e-04  2e+01     1.8     0.10 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.960              
## patch_sz_ml -0.880  0.844       
## dy:ptch_sz_  0.846 -0.881 -0.960
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model") 

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -18.6 < 0.001 **** very strong

 
 
 

Reduced model (with patch_size_ml)
reduced_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     66.5     83.9    -26.3     52.5       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.72  -0.57   0.03   0.66   2.49 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-01    0.36          
##             day         6e-04    0.02     -1.00
##  Residual               9e-02    0.30          
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##               Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)      1.196      0.142 24.725       8    9e-09 ***
## day             -0.018      0.008 14.809      -2     0.03 *  
## patch_size_ml    0.018      0.003 53.014       7    2e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.871       
## patch_sz_ml -0.423 -0.010
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model") 

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -17.7 < 0.001 **** very strong
results_table = fill.results.table(results_table,
                                   response_variable,
                                   patch_type_i,
                                   model_stats_full,
                                   model_stats_fixed)

Richness

response_variable = "species_richness"
plot.patches.points(ds_patches,
                    patch_type_i,
                    response_variable)

Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
                       response_variable)
save_results_in_table = TRUE

 

Filter
filtered_data = ds_patches %>%
  filter(
    metaecosystem == "no",
    time_point >= first_time_point_model,
    time_point <= last_time_point_model,
    !is.na(!!sym(response_variable))
  )
Plot filtered data
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable)

Analysis
Null model
null_model = lmer(
  get(response_variable) ~
    day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_ml and patch_size_ml : day)
full_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    patch_size_ml : day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    351.4    371.4   -167.7    335.4       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.1   -0.8   -0.1    0.5    2.2 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-02    0.13          
##             day         1e-04    0.01     -1.00
##  Residual               2e+00    1.56          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                   Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)          8.290      0.969 80.981     8.6    6e-13 ***
## day                 -0.294      0.051 44.221    -5.8    6e-07 ***
## patch_size_ml       -0.026      0.038 80.981    -0.7      0.5    
## day:patch_size_ml    0.008      0.002 44.221     3.9    3e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.934              
## patch_sz_ml -0.878  0.821       
## dy:ptch_sz_  0.821 -0.878 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model") 

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1      -25 < 0.001 **** very strong

 
 
 

Reduced model (with patch_size_ml)
reduced_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    360.9    378.4   -173.4    346.9       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.78  -0.75  -0.04   0.57   1.84 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1.804    1.34          
##             day         0.007    0.09     -1.00
##  Residual               2.478    1.57          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##               Estimate Std. Error    df t value Pr(>|t|)    
## (Intercept)       5.35       0.66 29.20       8    6e-09 ***
## day              -0.12       0.03 15.34      -4    0.002 ** 
## patch_size_ml     0.11       0.01 52.47       8    6e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.843       
## patch_sz_ml -0.473  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model") 

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -15.6 < 0.001 **** very strong
results_table = fill.results.table(results_table,
                                   response_variable,
                                   patch_type_i,
                                   model_stats_full,
                                   model_stats_fixed)

Evenness

response_variable = "evenness_pielou"
plot.patches.points(ds_patches,
                    patch_type_i,
                    response_variable)

Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
                       response_variable)
## Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE

 

Filter
filtered_data = ds_patches %>%
  filter(
    metaecosystem == "no",
    time_point >= first_time_point_model,
    time_point <= last_time_point_model,
    !is.na(!!sym(response_variable))
  )
Plot filtered data
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable)

Analysis
Null model
null_model = lmer(
  get(response_variable) ~
    day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_ml and patch_size_ml : day)
full_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    patch_size_ml : day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -82.1    -62.4     49.1    -98.1       79 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.4   -0.5    0.0    0.6    2.8 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.121         
##             day         7e-05    0.009    -1.00
##  Residual               2e-02    0.127         
## Number of obs: 87, groups:  culture_ID, 15
## 
## Fixed effects:
##                   Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)          8e-01      1e-01  2e+01     7.2    7e-07 ***
## day                  2e-03      6e-03  2e+01     0.4      0.7    
## patch_size_ml        2e-03      4e-03  2e+01     0.5      0.6    
## day:patch_size_ml   -1e-04      2e-04  2e+01    -0.5      0.6    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.954              
## patch_sz_ml -0.882  0.840       
## dy:ptch_sz_  0.845 -0.884 -0.954
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model") 

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.8   0.893     none

 
 
 

Reduced model (with patch_size_ml)
reduced_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -83.9    -66.6     48.9    -97.9       80 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.4   -0.5    0.0    0.6    2.8 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.122         
##             day         7e-05    0.009    -1.00
##  Residual               2e-02    0.127         
## Number of obs: 87, groups:  culture_ID, 15
## 
## Fixed effects:
##               Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)      8e-01      6e-02  3e+01    14.1    2e-15 ***
## day             -3e-04      3e-03  2e+01    -0.1      0.9    
## patch_size_ml    8e-05      1e-03  5e+01     0.1      0.9    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.830       
## patch_sz_ml -0.469 -0.027
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model") 

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.947     none
results_table = fill.results.table(results_table,
                                   response_variable,
                                   patch_type_i,
                                   model_stats_full,
                                   model_stats_fixed)

Median Size

response_variable = "median_body_area_µm2"
plot.patches.points(ds_patches,
                    patch_type_i,
                    response_variable)

Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
                       response_variable)
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE

 

Filter
filtered_data = ds_patches %>%
  filter(
    metaecosystem == "no",
    time_point >= first_time_point_model,
    time_point <= last_time_point_model,
    !is.na(!!sym(response_variable))
  )
Plot filtered data
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable)

Analysis
Null model
null_model = lmer(
  get(response_variable) ~
    day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_size_ml and patch_size_ml : day)
full_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    patch_size_ml : day +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1365.8   1385.7   -674.9   1349.8       81 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.93  -0.59  -0.05   0.61   2.21 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e+03     39           
##             day         2e+01      4      -1.00
##  Residual               2e+05    473           
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                   Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)         3130.1      299.2   77.6    10.5   <2e-16 ***
## day                   28.1       15.9   51.0     1.8     0.08 .  
## patch_size_ml          3.7       11.6   77.3     0.3     0.75    
## day:patch_size_ml     -0.3        0.6   50.8    -0.4     0.67    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    ptch__
## day         -0.933              
## patch_sz_ml -0.881  0.821       
## dy:ptch_sz_  0.825 -0.883 -0.933
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model") 

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.8   0.892     none

 
 
 

Reduced model (with patch_size_ml)
reduced_model = lmer(
  get(response_variable) ~
    day +
    patch_size_ml +
    (day | culture_ID),
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1363.9   1381.4   -675.0   1349.9       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.90  -0.54  -0.05   0.66   2.26 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e+03     47           
##             day         2e+01      5      -1.00
##  Residual               2e+05    473           
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##               Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)     3235.5      169.3   82.9    19.1   <2e-16 ***
## day               22.1        7.5   45.9     2.9    0.005 ** 
## patch_size_ml     -0.9        4.2   33.6    -0.2    0.829    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.771       
## patch_sz_ml -0.546 -0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" ID: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Patch Type: ", filtered_data$patch_type, "<br>",
                         "Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
                         "Species richness: ", filtered_data$species_richness, "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        title = "Residuals vs. Fitted Values",
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model") 

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.829     none
results_table = fill.results.table(results_table,
                                   response_variable,
                                   patch_type_i,
                                   model_stats_full,
                                   model_stats_fixed)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Small isolated"

## [1] "Medium isolated"

## [1] "Large isolated"

S_L vs S_S

patch_type_i = c("Small connected to large",
                 "Small connected to small")

Biomass

response_variable = "bioarea_mm2_per_ml"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    147.6    167.6    -65.8    131.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.99  -0.75   0.07   0.67   2.18 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-01    0.50          
##             day         4e-04    0.02     -1.00
##  Residual               2e-01    0.47          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               3.595      0.234 16.432    15.4
## day                                      -0.136      0.011 21.354   -12.4
## patch_typeSmall connected to large        0.352      0.405 16.432     0.9
## day:patch_typeSmall connected to large    0.009      0.019 21.354     0.5
##                                        Pr(>|t|)    
## (Intercept)                               4e-11 ***
## day                                       3e-11 ***
## patch_typeSmall connected to large          0.4    
## day:patch_typeSmall connected to large      0.7    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.952              
## ptch_tySctl -0.577  0.550       
## dy:ptc_Sctl  0.550 -0.577 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            43.056  43.056     1 21.354 191.7506 3.851e-12 ***
## patch_type      0.170   0.170     1 16.432   0.7565    0.3970    
## day:patch_type  0.046   0.046     1 21.354   0.2058    0.6546    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1    -10.4   0.001 *** strong

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    145.8    163.3    -65.9    131.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.98  -0.74   0.04   0.72   2.13 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         4e-04    0.02     -1.00
##  Residual               2e-01    0.47          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           3.537      0.196 17.527      18    9e-13
## day                                  -0.133      0.009 21.095     -15    1e-12
## patch_typeSmall connected to large    0.528      0.123 32.918       4    2e-04
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.932       
## ptch_tySctl -0.210  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -12.2 < 0.001 **** very strong
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    147.6    167.6    -65.8    131.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.99  -0.75   0.07   0.67   2.18 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-01    0.50          
##             day         4e-04    0.02     -1.00
##  Residual               2e-01    0.47          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               3.595      0.234 16.432    15.4
## day                                      -0.136      0.011 21.354   -12.4
## patch_typeSmall connected to large        0.352      0.405 16.432     0.9
## day:patch_typeSmall connected to large    0.009      0.019 21.354     0.5
##                                        Pr(>|t|)    
## (Intercept)                               4e-11 ***
## day                                       3e-11 ***
## patch_typeSmall connected to large          0.4    
## day:patch_typeSmall connected to large      0.7    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.952              
## ptch_tySctl -0.577  0.550       
## dy:ptc_Sctl  0.550 -0.577 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            43.056  43.056     1 21.354 191.7506 3.851e-12 ***
## patch_type      0.170   0.170     1 16.432   0.7565    0.3970    
## day:patch_type  0.046   0.046     1 21.354   0.2058    0.6546    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1    -10.4   0.001 *** strong

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    145.8    163.3    -65.9    131.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.98  -0.74   0.04   0.72   2.13 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         4e-04    0.02     -1.00
##  Residual               2e-01    0.47          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           3.537      0.196 17.527      18    9e-13
## day                                  -0.133      0.009 21.095     -15    1e-12
## patch_typeSmall connected to large    0.528      0.123 32.918       4    2e-04
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.932       
## ptch_tySctl -0.210  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -12.2 < 0.001 **** very strong
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Shannon

response_variable = "shannon"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     61.6     81.6    -22.8     45.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.3   -0.5    0.1    0.5    2.2 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-03    0.032         
##             day         1e-05    0.004    -1.00
##  Residual               1e-01    0.309         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               1.742      0.113 76.059      15
## day                                      -0.043      0.006 53.128      -7
## patch_typeSmall connected to large       -0.355      0.195 76.059      -2
## day:patch_typeSmall connected to large    0.043      0.010 53.128       4
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                       1e-09 ***
## patch_typeSmall connected to large         0.07 .  
## day:patch_typeSmall connected to large    1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.930              
## ptch_tySctl -0.577  0.537       
## dy:ptc_Sctl  0.537 -0.577 -0.930
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            1.75046 1.75046     1 53.128 18.3720 7.684e-05 ***
## patch_type     0.31502 0.31502     1 76.059  3.3063 0.0729497 .  
## day:patch_type 1.63469 1.63469     1 53.128 17.1569 0.0001239 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -17.5 < 0.001 **** very strong

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     72.8     90.3    -29.4     58.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.2   -0.5    0.2    0.6    2.1 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 6e-02    0.24          
##             day         3e-04    0.02     -1.00
##  Residual               1e-01    0.32          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           1.510      0.115 23.192      13    3e-12
## day                                  -0.029      0.007 15.944      -4    4e-04
## patch_typeSmall connected to large    0.341      0.075 39.923       4    5e-05
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.926       
## ptch_tySctl -0.218  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1     -6.3   0.004 *** strong
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Richness

response_variable = "species_richness"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    292.1    312.1   -138.1    276.1       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.9   -0.6   -0.1    0.5    2.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00    0e+00        
##             day         2e-16    1e-08     NaN
##  Residual               1e+00    1e+00        
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                7.69       0.41 90.00    18.8
## day                                       -0.22       0.02 90.00   -10.1
## patch_typeSmall connected to large        -0.48       0.71 90.00    -0.7
## day:patch_typeSmall connected to large     0.12       0.04 90.00     3.2
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                      <2e-16 ***
## patch_typeSmall connected to large        0.500    
## day:patch_typeSmall connected to large    0.002 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.935              
## ptch_tySctl -0.577  0.540       
## dy:ptc_Sctl  0.540 -0.577 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## day            91.772  91.772     1    90 72.8881 3.151e-13 ***
## patch_type      0.578   0.578     1    90  0.4589  0.499857    
## day:patch_type 12.652  12.652     1    90 10.0485  0.002084 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -20.5 < 0.001 **** very strong

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    299.1    316.6   -142.6    285.1       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.83  -0.68  -0.06   0.50   2.54 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.619    0.79          
##             day         0.001    0.04     -1.00
##  Residual               1.323    1.15          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            6.97       0.41 14.53      17    5e-11
## day                                   -0.18       0.02 15.66      -9    2e-07
## patch_typeSmall connected to large     1.68       0.26 49.18       6    5e-08
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.929       
## ptch_tySctl -0.214  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value         evidence
## 1    -13.5 < 0.001 **** very strong
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Evenness

response_variable = "evenness_pielou"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -115.6    -96.2     65.8   -131.6       76 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.4    0.1    0.7    1.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00    0e+00        
##             day         7e-18    3e-09     NaN
##  Residual               1e-02    1e-01        
## Number of obs: 84, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               0.743      0.042 84.000      18
## day                                       0.004      0.002 84.000       2
## patch_typeSmall connected to large       -0.071      0.070 84.000      -1
## day:patch_typeSmall connected to large    0.005      0.004 84.000       1
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                        0.07 .  
## patch_typeSmall connected to large         0.31    
## day:patch_typeSmall connected to large     0.22    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.932              
## ptch_tySctl -0.590  0.550       
## dy:ptc_Sctl  0.561 -0.602 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF DenDF F value    Pr(>F)    
## day            0.142288 0.142288     1    84 11.6451 0.0009925 ***
## patch_type     0.012547 0.012547     1    84  1.0269 0.3138074    
## day:patch_type 0.018337 0.018337     1    84  1.5007 0.2239857    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.4   0.445     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -116.1    -99.1     65.1   -130.1       77 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.5    0.1    0.7    1.8 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00    0e+00        
##             day         1e-15    3e-08     NaN
##  Residual               1e-02    1e-01        
## Number of obs: 84, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                           7e-01      4e-02 8e+01    20.6   <2e-16
## day                                   6e-03      2e-03 8e+01     3.2    0.002
## patch_typeSmall connected to large    9e-03      3e-02 8e+01     0.4    0.718
##                                       
## (Intercept)                        ***
## day                                ** 
## patch_typeSmall connected to large    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.899       
## ptch_tySctl -0.224 -0.042
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.717     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Median Size

response_variable = "median_body_area_µm2"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1420.1   1440.1   -702.1   1404.1       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.90  -0.66   0.02   0.73   1.90 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e+04    307           
##             day         3e+02     19      -1.00
##  Residual               3e+05    578           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                               3e+03      2e+02 2e+01    13.2
## day                                       4e+01      1e+01 2e+01     2.9
## patch_typeSmall connected to large        1e+02      4e+02 2e+01     0.3
## day:patch_typeSmall connected to large    5e-01      2e+01 2e+01     0.0
##                                        Pr(>|t|)    
## (Intercept)                               1e-11 ***
## day                                        0.01 ** 
## patch_typeSmall connected to large         0.75    
## day:patch_typeSmall connected to large     0.98    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Sctl
## day         -0.946              
## ptch_tySctl -0.577  0.546       
## dy:ptc_Sctl  0.546 -0.577 -0.946
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## day            3897822 3897822     1 15.70 11.6655 0.003625 **
## patch_type       34717   34717     1 20.88  0.1039 0.750402   
## day:patch_type     216     216     1 15.70  0.0006 0.980048   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.9   0.567     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1418.1   1435.6   -702.1   1404.1       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.90  -0.66   0.02   0.73   1.89 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e+04    307           
##             day         3e+02     19      -1.00
##  Residual               3e+05    578           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error   df t value Pr(>|t|)
## (Intercept)                            3056        194   23      16    8e-14
## day                                      37         10   16       4    0.002
## patch_typeSmall connected to large      139        130   70       1    0.288
##                                       
## (Intercept)                        ***
## day                                ** 
## patch_typeSmall connected to large    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.922       
## ptch_tySctl -0.223  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.9   0.287     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Small connected to large"

## [1] "Small connected to small"

S_S vs S

patch_type_i = c("Small connected to small",
                 "Small isolated")

Biomass

response_variable = "bioarea_mm2_per_ml"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    179.0    199.0    -81.5    163.0       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.7   -0.7    0.0    0.6    3.6 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         5e-04    0.02     -1.00
##  Residual               3e-01    0.57          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                3.92       0.37 16.09    10.5
## day                                       -0.15       0.02 21.17    -8.3
## patch_typeSmall connected to small        -0.33       0.46 16.09    -0.7
## day:patch_typeSmall connected to small     0.01       0.02 21.17     0.7
##                                        Pr(>|t|)    
## (Intercept)                               1e-08 ***
## day                                       4e-08 ***
## patch_typeSmall connected to small          0.5    
## day:patch_typeSmall connected to small      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.952              
## ptch_tyScts -0.816  0.777       
## dy:ptc_Scts  0.777 -0.816 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            54.390  54.390     1 21.167 165.4017 1.797e-11 ***
## patch_type      0.167   0.167     1 16.090   0.5089    0.4859    
## day:patch_type  0.147   0.147     1 21.167   0.4457    0.5116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.5   0.778     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    177.5    195.0    -81.7    163.5       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.6   -0.7    0.0    0.6    3.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.53          
##             day         5e-04    0.02     -1.00
##  Residual               3e-01    0.57          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            3.73       0.24 20.52    15.7    7e-13
## day                                   -0.14       0.01 20.57   -13.3    2e-11
## patch_typeSmall connected to small    -0.04       0.14 41.57    -0.2      0.8
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.876       
## ptch_tyScts -0.393  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.805     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    179.0    199.0    -81.5    163.0       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.7   -0.7    0.0    0.6    3.6 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         5e-04    0.02     -1.00
##  Residual               3e-01    0.57          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                3.92       0.37 16.09    10.5
## day                                       -0.15       0.02 21.17    -8.3
## patch_typeSmall connected to small        -0.33       0.46 16.09    -0.7
## day:patch_typeSmall connected to small     0.01       0.02 21.17     0.7
##                                        Pr(>|t|)    
## (Intercept)                               1e-08 ***
## day                                       4e-08 ***
## patch_typeSmall connected to small          0.5    
## day:patch_typeSmall connected to small      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.952              
## ptch_tyScts -0.816  0.777       
## dy:ptc_Scts  0.777 -0.816 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## day            54.390  54.390     1 21.167 165.4017 1.797e-11 ***
## patch_type      0.167   0.167     1 16.090   0.5089    0.4859    
## day:patch_type  0.147   0.147     1 21.167   0.4457    0.5116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.5   0.778     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    177.5    195.0    -81.7    163.5       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.6   -0.7    0.0    0.6    3.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.53          
##             day         5e-04    0.02     -1.00
##  Residual               3e-01    0.57          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            3.73       0.24 20.52    15.7    7e-13
## day                                   -0.14       0.01 20.57   -13.3    2e-11
## patch_typeSmall connected to small    -0.04       0.14 41.57    -0.2      0.8
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.876       
## ptch_tyScts -0.393  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.805     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Shannon

response_variable = "shannon"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     89.5    109.4    -36.7     73.5       81 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.86  -0.55   0.06   0.58   2.40 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.11          
##             day         1e-04    0.01     -1.00
##  Residual               1e-01    0.35          
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               1.787      0.193 47.907     9.3
## day                                      -0.048      0.011 26.275    -4.4
## patch_typeSmall connected to small       -0.045      0.234 47.492    -0.2
## day:patch_typeSmall connected to small    0.004      0.013 25.695     0.3
##                                        Pr(>|t|)    
## (Intercept)                               3e-12 ***
## day                                       2e-04 ***
## patch_typeSmall connected to small          0.8    
## day:patch_typeSmall connected to small      0.7    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.928              
## ptch_tyScts -0.822  0.763       
## dy:ptc_Scts  0.767 -0.826 -0.927
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            5.9530  5.9530     1 25.695 47.6357 2.674e-07 ***
## patch_type     0.0046  0.0046     1 47.492  0.0370    0.8484    
## day:patch_type 0.0133  0.0133     1 25.695  0.1067    0.7465    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.8   0.907     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     87.6    105.0    -36.8     73.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.86  -0.57   0.08   0.65   2.33 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.11          
##             day         1e-04    0.01     -1.00
##  Residual               1e-01    0.35          
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           1.738      0.124 67.649    14.0   <2e-16
## day                                  -0.045      0.006 24.893    -7.3    1e-07
## patch_typeSmall connected to small    0.026      0.087 33.116     0.3      0.8
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.815       
## ptch_tyScts -0.461 -0.016
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.767     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Richness

response_variable = "species_richness"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    333.5    353.5   -158.7    317.5       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.3   -0.8   -0.2    0.5    2.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e-11    1e-05         
##             day         4e-13    7e-07    -1.00
##  Residual               2e+00    1e+00         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                8.32       0.73 90.00    11.4
## day                                       -0.25       0.04 90.00    -6.7
## patch_typeSmall connected to small        -0.63       0.89 90.00    -0.7
## day:patch_typeSmall connected to small     0.04       0.05 90.00     0.8
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                       2e-09 ***
## patch_typeSmall connected to small          0.5    
## day:patch_typeSmall connected to small      0.4    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.935              
## ptch_tyScts -0.816  0.763       
## dy:ptc_Scts  0.763 -0.816 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF DenDF  F value Pr(>F)    
## day            204.298 204.298     1    90 102.4855 <2e-16 ***
## patch_type       1.004   1.004     1    90   0.5036 0.4797    
## day:patch_type   1.338   1.338     1    90   0.6710 0.4149    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.3   0.707     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    332.2    349.7   -159.1    318.2       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.3   -0.8   -0.1    0.6    2.5 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e-10    3e-05         
##             day         5e-12    2e-06    -1.00
##  Residual               2e+00    1e+00         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            7.86       0.47 90.00    16.7   <2e-16
## day                                   -0.23       0.02 90.00   -10.4   <2e-16
## patch_typeSmall connected to small     0.05       0.32 90.00     0.2      0.9
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.836       
## ptch_tyScts -0.448  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.875     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Evenness

response_variable = "evenness_pielou"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -68.5    -49.3     42.2    -84.5       73 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.7   -0.3    0.0    0.6    2.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 7e-04    0.027         
##             day         8e-06    0.003    -1.00
##  Residual               2e-02    0.141         
## Number of obs: 81, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               0.870      0.076 61.647      12
## day                                      -0.005      0.004 29.903      -1
## patch_typeSmall connected to small       -0.128      0.093 61.775      -1
## day:patch_typeSmall connected to small    0.009      0.005 30.919       2
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                        0.25    
## patch_typeSmall connected to small         0.17    
## day:patch_typeSmall connected to small     0.09 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.925              
## ptch_tyScts -0.815  0.753       
## dy:ptc_Scts  0.755 -0.816 -0.925
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            0.000460 0.000460     1 30.919  0.0231 0.88009  
## patch_type     0.037879 0.037879     1 61.775  1.9063 0.17235  
## day:patch_type 0.060612 0.060612     1 30.919  3.0505 0.09064 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        1    0.22     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    -67.7    -50.9     40.8    -81.7       74 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.8   -0.4    0.1    0.7    2.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-03    0.048         
##             day         2e-05    0.004    -1.00
##  Residual               2e-02    0.141         
## Number of obs: 81, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                           8e-01      5e-02 6e+01    15.1   <2e-16
## day                                   1e-03      3e-03 2e+01     0.5      0.6
## patch_typeSmall connected to small    2e-02      4e-02 3e+01     0.5      0.6
##                                       
## (Intercept)                        ***
## day                                   
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.816       
## ptch_tyScts -0.464 -0.010
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.8   0.662     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Median Size

response_variable = "median_body_area_µm2"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1420.0   1439.9   -702.0   1404.0       81 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.53  -0.60  -0.08   0.86   1.81 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e+05    341           
##             day         5e+02     23      -1.00
##  Residual               4e+05    625           
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error   df t value
## (Intercept)                                3143        362   24     8.7
## day                                          24         20   18     1.2
## patch_typeSmall connected to small          -84        441   24    -0.2
## day:patch_typeSmall connected to small       12         24   17     0.5
##                                        Pr(>|t|)    
## (Intercept)                               6e-09 ***
## day                                         0.3    
## patch_typeSmall connected to small          0.9    
## day:patch_typeSmall connected to small      0.6    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Scts
## day         -0.945              
## ptch_tyScts -0.821  0.776       
## dy:ptc_Scts  0.780 -0.825 -0.944
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            2363748 2363748     1 17.341  6.0523 0.02466 *
## patch_type       14117   14117     1 24.108  0.0361 0.85080  
## day:patch_type  101258  101258     1 17.341  0.2593 0.61704  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        3   0.599     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1418.2   1435.6   -702.1   1404.2       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.47  -0.57  -0.07   0.85   1.83 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e+05    347           
##             day         5e+02     23      -1.00
##  Residual               4e+05    626           
## Number of obs: 89, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error   df t value Pr(>|t|)
## (Intercept)                            2999        228   34    13.2    7e-15
## day                                      32         12   17     2.8     0.01
## patch_typeSmall connected to small      128        145   56     0.9     0.38
##                                       
## (Intercept)                        ***
## day                                *  
## patch_typeSmall connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.853       
## ptch_tyScts -0.412 -0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.2   0.381     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Small connected to small"

## [1] "Small isolated"

Medium patches

patch_type_i = c("Medium isolated",
                     "Medium connected to medium")

Biomass

response_variable = "bioarea_mm2_per_ml"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    281.6    301.6   -132.8    265.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.37  -0.65  -0.05   0.66   2.10 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         3e-04    0.02     -1.00
##  Residual               1e+00    1.04          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                          Estimate Std. Error     df t value
## (Intercept)                                 5.119      0.580 17.638     8.8
## day                                        -0.170      0.029 25.546    -5.9
## patch_typeMedium connected to medium        0.915      0.711 17.638     1.3
## day:patch_typeMedium connected to medium   -0.004      0.035 25.546    -0.1
##                                          Pr(>|t|)    
## (Intercept)                                 7e-08 ***
## day                                         4e-06 ***
## patch_typeMedium connected to medium          0.2    
## day:patch_typeMedium connected to medium      0.9    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Mctm
## day         -0.936              
## ptch_tyMctm -0.816  0.765       
## dy:ptc_Mctm  0.765 -0.816 -0.936
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            101.588 101.588     1 25.546 94.2646 4.667e-10 ***
## patch_type       1.787   1.787     1 17.638  1.6580    0.2145    
## day:patch_type   0.015   0.015     1 25.546  0.0135    0.9084    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1     -4.6   0.014 ** moderate

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    279.6    297.1   -132.8    265.6       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.37  -0.66  -0.05   0.65   2.11 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-01    0.51          
##             day         3e-04    0.02     -1.00
##  Residual               1e+00    1.04          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                      Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                              5.17       0.37 23.97      14    7e-13
## day                                     -0.17       0.02 25.46     -10    1e-10
## patch_typeMedium connected to medium     0.84       0.25 20.02       3    0.003
##                                         
## (Intercept)                          ***
## day                                  ***
## patch_typeMedium connected to medium ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.839       
## ptch_tyMctm -0.445  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value   evidence
## 1     -6.6   0.003 *** strong
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Shannon

response_variable = "shannon"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     47.2     67.2    -15.6     31.2       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.3   -0.6    0.1    0.7    1.9 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-02    0.183         
##             day         4e-05    0.006    -1.00
##  Residual               8e-02    0.278         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                          Estimate Std. Error     df t value
## (Intercept)                                 1.182      0.165 20.526     7.2
## day                                         0.009      0.008 34.267     1.1
## patch_typeMedium connected to medium        0.179      0.202 20.526     0.9
## day:patch_typeMedium connected to medium   -0.010      0.010 34.267    -1.0
##                                          Pr(>|t|)    
## (Intercept)                                 5e-07 ***
## day                                           0.3    
## patch_typeMedium connected to medium          0.4    
## day:patch_typeMedium connected to medium      0.3    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Mctm
## day         -0.936              
## ptch_tyMctm -0.816  0.764       
## dy:ptc_Mctm  0.764 -0.816 -0.936
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## day            0.048608 0.048608     1 34.267  0.6280 0.4336
## patch_type     0.060641 0.060641     1 20.526  0.7834 0.3863
## day:patch_type 0.082004 0.082004     1 34.267  1.0594 0.3106
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.9   0.578     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     46.2     63.7    -16.1     32.2       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.4   -0.6    0.2    0.7    1.9 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 4e-02    0.194         
##             day         5e-05    0.007    -1.00
##  Residual               8e-02    0.279         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                      Estimate Std. Error     df t value
## (Intercept)                             1.313      0.108 24.036    12.2
## day                                     0.002      0.005 30.559     0.5
## patch_typeMedium connected to medium   -0.017      0.071 25.203    -0.2
##                                      Pr(>|t|)    
## (Intercept)                             9e-12 ***
## day                                       0.6    
## patch_typeMedium connected to medium      0.8    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.843       
## ptch_tyMctm -0.440  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.817     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Richness

response_variable = "species_richness"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    354.3    374.3   -169.1    338.3       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.1   -0.6    0.0    0.8    2.5 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-01    0.462         
##             day         3e-06    0.002    -1.00
##  Residual               2e+00    1.534         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                          Estimate Std. Error    df t value
## (Intercept)                                  7.28       0.82 41.14     8.9
## day                                         -0.09       0.04 74.62    -2.1
## patch_typeMedium connected to medium         0.41       1.00 41.14     0.4
## day:patch_typeMedium connected to medium     0.02       0.05 74.62     0.4
##                                          Pr(>|t|)    
## (Intercept)                                 4e-11 ***
## day                                          0.04 *  
## patch_typeMedium connected to medium         0.69    
## day:patch_typeMedium connected to medium     0.69    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Mctm
## day         -0.909              
## ptch_tyMctm -0.816  0.742       
## dy:ptc_Mctm  0.742 -0.816 -0.909
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## day            21.3935 21.3935     1 74.621  9.0856 0.003519 **
## patch_type      0.3898  0.3898     1 41.136  0.1655 0.686222   
## day:patch_type  0.3732  0.3732     1 74.621  0.1585 0.691689   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.8     0.2     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    352.4    369.9   -169.2    338.4       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.10  -0.61  -0.03   0.73   2.56 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-01    0.461         
##             day         3e-06    0.002    -1.00
##  Residual               2e+00    1.536         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                      Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                              7.03       0.55 44.26      13   <2e-16
## day                                     -0.07       0.02 74.60      -3    0.003
## patch_typeMedium connected to medium     0.77       0.42 15.01       2    0.084
##                                         
## (Intercept)                          ***
## day                                  ** 
## patch_typeMedium connected to medium .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.784       
## ptch_tyMctm -0.507  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.1    0.08   * weak
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Evenness

response_variable = "evenness_pielou"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -106.3    -86.3     61.2   -122.3       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.2   -0.7    0.2    0.8    1.7 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 8e-12    3e-06         
##             day         2e-14    1e-07    -1.00
##  Residual               2e-02    1e-01         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                          Estimate Std. Error     df t value
## (Intercept)                                 0.618      0.063 90.000     9.8
## day                                         0.010      0.003 90.000     3.1
## patch_typeMedium connected to medium        0.044      0.077 90.000     0.6
## day:patch_typeMedium connected to medium   -0.006      0.004 90.000    -1.6
##                                          Pr(>|t|)    
## (Intercept)                                 8e-16 ***
## day                                         0.002 ** 
## patch_typeMedium connected to medium        0.573    
## day:patch_typeMedium connected to medium    0.113    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Mctm
## day         -0.935              
## ptch_tyMctm -0.816  0.763       
## dy:ptc_Mctm  0.763 -0.816 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF DenDF F value    Pr(>F)    
## day            0.184092 0.184092     1    90 12.2383 0.0007298 ***
## patch_type     0.004812 0.004812     1    90  0.3199 0.5730908    
## day:patch_type 0.038515 0.038515     1    90  2.5605 0.1130708    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1       -5   0.011 ** moderate

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -105.8    -88.3     59.9   -119.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -2.4   -0.7    0.1    0.8    1.6 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-12    1e-06         
##             day         3e-15    6e-08    -1.00
##  Residual               2e-02    1e-01         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                      Estimate Std. Error     df t value
## (Intercept)                             0.695      0.041 90.000      17
## day                                     0.006      0.002 90.000       3
## patch_typeMedium connected to medium   -0.072      0.028 90.000      -3
##                                      Pr(>|t|)    
## (Intercept)                            <2e-16 ***
## day                                     0.003 ** 
## patch_typeMedium connected to medium    0.011 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.836       
## ptch_tyMctm -0.448  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1     -4.4   0.011 ** moderate
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Median Size

response_variable = "median_body_area_µm2"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+01
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1441.4   1461.4   -712.7   1425.4       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.4   -0.4    0.0    0.5    4.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00      0          
##             day         1e+02     11       NaN
##  Residual               4e+05    636          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                          Estimate Std. Error   df t value
## (Intercept)                                  3278        327   75    10.0
## day                                            24         18   79     1.3
## patch_typeMedium connected to medium           82        400   75     0.2
## day:patch_typeMedium connected to medium      -11         22   79    -0.5
##                                          Pr(>|t|)    
## (Intercept)                                 2e-15 ***
## day                                           0.2    
## patch_typeMedium connected to medium          0.8    
## day:patch_typeMedium connected to medium      0.6    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Mctm
## day         -0.895              
## ptch_tyMctm -0.816  0.731       
## dy:ptc_Mctm  0.731 -0.816 -0.895
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            1129166 1129166     1 79.079  2.7954 0.09849 .
## patch_type       16826   16826     1 75.000  0.0417 0.83883  
## day:patch_type  109948  109948     1 79.079  0.2722 0.60333  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.5   0.763     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1439.6   1457.1   -712.8   1425.6       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -4.4   -0.4    0.1    0.4    4.1 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e+03     53           
##             day         2e+02     14      -1.00
##  Residual               4e+05    636           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                      Estimate Std. Error   df t value Pr(>|t|)
## (Intercept)                              3401        223   88    15.3   <2e-16
## day                                        16         10   38     1.6      0.1
## patch_typeMedium connected to medium     -103        176   20    -0.6      0.6
##                                         
## (Intercept)                          ***
## day                                     
## patch_typeMedium connected to medium    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.765       
## ptch_tyMctm -0.526  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.7   0.564     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Medium isolated"

## [1] "Medium connected to medium"

L_S vs L_L

patch_type_i = c("Large connected to small",
                 "Large connected to large")

Biomass

response_variable = "bioarea_mm2_per_ml"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    240.8    260.8   -112.4    224.8       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.23  -0.62   0.02   0.51   2.20 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.917    0.96          
##             day         0.002    0.04     -1.00
##  Residual               0.627    0.79          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               5.475      0.418 15.044    13.1
## day                                      -0.147      0.020 15.669    -7.2
## patch_typeLarge connected to small       -0.027      0.724 15.044     0.0
## day:patch_typeLarge connected to small   -0.007      0.035 15.669    -0.2
##                                        Pr(>|t|)    
## (Intercept)                               1e-09 ***
## day                                       2e-06 ***
## patch_typeLarge connected to small          1.0    
## day:patch_typeLarge connected to small      0.8    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lcts
## day         -0.965              
## ptch_tyLcts -0.577  0.557       
## dy:ptc_Lcts  0.557 -0.577 -0.965
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            45.506  45.506     1 15.669 72.5264 2.856e-07 ***
## patch_type      0.001   0.001     1 15.044  0.0014    0.9712    
## day:patch_type  0.023   0.023     1 15.669  0.0372    0.8495    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.2   0.685     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    238.8    256.3   -112.4    224.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.23  -0.62   0.02   0.50   2.18 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.921    0.96          
##             day         0.002    0.04     -1.00
##  Residual               0.627    0.79          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            5.52       0.35 16.00    15.9    3e-11
## day                                   -0.15       0.02 15.66    -9.0    1e-07
## patch_typeLarge connected to small    -0.16       0.19 40.65    -0.9      0.4
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeLarge connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.949       
## ptch_tyLcts -0.182  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.3   0.397     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Shannon

response_variable = "shannon"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     23.2     43.2     -3.6      7.2       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.8   -0.5    0.0    0.7    1.8 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 5e-03    0.074         
##             day         2e-05    0.004    -1.00
##  Residual               6e-02    0.250         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               1.500      0.094 25.418    16.0
## day                                      -0.004      0.005 16.288    -0.8
## patch_typeLarge connected to small       -0.207      0.163 25.418    -1.3
## day:patch_typeLarge connected to small    0.011      0.009 16.288     1.3
##                                        Pr(>|t|)    
## (Intercept)                               9e-15 ***
## day                                         0.5    
## patch_typeLarge connected to small          0.2    
## day:patch_typeLarge connected to small      0.2    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lcts
## day         -0.939              
## ptch_tyLcts -0.577  0.542       
## dy:ptc_Lcts  0.542 -0.577 -0.939
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## day            0.012907 0.012907     1 16.288  0.2065 0.6555
## patch_type     0.100997 0.100997     1 25.418  1.6161 0.2151
## day:patch_type 0.110436 0.110436     1 16.288  1.7671 0.2021
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      2.3   0.431     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##     22.9     40.4     -4.5      8.9       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.8   -0.5    0.1    0.7    1.9 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.114         
##             day         5e-05    0.007    -1.00
##  Residual               6e-02    0.250         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           1e+00      8e-02  2e+01    17.4    7e-15
## day                                   5e-05      4e-03  2e+01     0.0      1.0
## patch_typeLarge connected to small   -5e-03      6e-02  7e+01    -0.1      0.9
##                                       
## (Intercept)                        ***
## day                                   
## patch_typeLarge connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.919       
## ptch_tyLcts -0.228  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.932     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Richness

response_variable = "species_richness"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    319.0    339.0   -151.5    303.0       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.6   -0.5    0.1    0.5    2.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.127    0.36          
##             day         0.001    0.04     -1.00
##  Residual               1.574    1.25          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                7.22       0.47 48.53    15.3
## day                                       -0.05       0.03 25.62    -1.8
## patch_typeLarge connected to small        -0.70       0.81 48.53    -0.9
## day:patch_typeLarge connected to small     0.04       0.05 25.62     1.0
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                        0.09 .  
## patch_typeLarge connected to small         0.40    
## day:patch_typeLarge connected to small     0.34    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lcts
## day         -0.921              
## ptch_tyLcts -0.577  0.532       
## dy:ptc_Lcts  0.532 -0.577 -0.921
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## day            1.8895  1.8895     1 25.619  1.2004 0.2834
## patch_type     1.1533  1.1533     1 48.528  0.7327 0.3962
## day:patch_type 1.4823  1.4823     1 25.619  0.9417 0.3409
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.1   0.628     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    317.9    335.4   -152.0    303.9       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.6   -0.5    0.2    0.5    2.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.168    0.41          
##             day         0.002    0.04     -1.00
##  Residual               1.583    1.26          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            6.97       0.40 50.73    17.3   <2e-16
## day                                   -0.03       0.02 23.41    -1.5      0.2
## patch_typeLarge connected to small     0.03       0.32 28.02     0.1      0.9
##                                       
## (Intercept)                        ***
## day                                   
## patch_typeLarge connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.891       
## ptch_tyLcts -0.262  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.933     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Evenness

response_variable = "evenness_pielou"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -148.7   -128.7     82.4   -164.7       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.5    0.2    0.6    2.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 6e-03    0.079         
##             day         2e-05    0.004    -1.00
##  Residual               9e-03    0.093         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               0.755      0.042 15.114    17.9
## day                                       0.002      0.002 15.145     0.7
## patch_typeLarge connected to small       -0.044      0.073 15.114    -0.6
## day:patch_typeLarge connected to small    0.002      0.004 15.145     0.7
##                                        Pr(>|t|)    
## (Intercept)                               1e-11 ***
## day                                         0.5    
## patch_typeLarge connected to small          0.6    
## day:patch_typeLarge connected to small      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lcts
## day         -0.958              
## ptch_tyLcts -0.577  0.553       
## dy:ptc_Lcts  0.553 -0.577 -0.958
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## day            0.0189475 0.0189475     1 15.145  2.1734 0.1609
## patch_type     0.0032234 0.0032234     1 15.114  0.3698 0.5522
## day:patch_type 0.0037138 0.0037138     1 15.145  0.4260 0.5237
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.6   0.809     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -150.3   -132.8     82.2   -164.3       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.4    0.2    0.6    2.1 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 7e-03    0.082         
##             day         2e-05    0.004    -1.00
##  Residual               9e-03    0.093         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                           7e-01      4e-02 2e+01    20.8    4e-13
## day                                   2e-03      2e-03 2e+01     1.3      0.2
## patch_typeLarge connected to small    1e-03      2e-02 7e+01     0.1      1.0
##                                       
## (Intercept)                        ***
## day                                   
## patch_typeLarge connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.940       
## ptch_tyLcts -0.197  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.953     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Median Size

response_variable = "median_body_area_µm2"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1399.6   1419.6   -691.8   1383.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.82  -0.67  -0.03   0.49   2.74 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 8e+04    278           
##             day         5e+02     23      -1.00
##  Residual               2e+05    498           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error   df t value
## (Intercept)                                2389        202   30    11.9
## day                                          72         12   19     6.1
## patch_typeLarge connected to small           47        349   30     0.1
## day:patch_typeLarge connected to small       -6         20   19    -0.3
##                                        Pr(>|t|)    
## (Intercept)                               8e-13 ***
## day                                       8e-06 ***
## patch_typeLarge connected to small          0.9    
## day:patch_typeLarge connected to small      0.8    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lcts
## day         -0.934              
## ptch_tyLcts -0.577  0.539       
## dy:ptc_Lcts  0.539 -0.577 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            11210561 11210561     1 19.055 45.1503 1.986e-06 ***
## patch_type         4563     4563     1 29.831  0.0184    0.8931    
## day:patch_type    18765    18765     1 19.055  0.0756    0.7863    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      3.8    0.91     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1397.7   1415.2   -691.9   1383.7       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.79  -0.66  -0.04   0.49   2.76 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 8e+04    280           
##             day         5e+02     23      -1.00
##  Residual               2e+05    498           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error   df t value Pr(>|t|)
## (Intercept)                            2419        170   34    14.2    7e-16
## day                                      70         10   19     7.2    8e-07
## patch_typeLarge connected to small      -42        125   37    -0.3      0.7
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeLarge connected to small    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.906       
## ptch_tyLcts -0.245  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.735     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Large connected to small"

## [1] "Large connected to large"

L_L vs L

patch_type_i = c("Large connected to large",
                 "Large isolated")

Biomass

response_variable = "bioarea_mm2_per_ml"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    247.1    267.1   -115.6    231.1       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -1.83  -0.64   0.01   0.51   2.37 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.325    0.57          
##             day         0.001    0.03     -0.83
##  Residual               0.635    0.80          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                4.95       0.48 15.00    10.2
## day                                       -0.09       0.03 15.00    -3.4
## patch_typeLarge connected to large         0.53       0.59 15.00     0.9
## day:patch_typeLarge connected to large    -0.06       0.03 15.00    -1.8
##                                        Pr(>|t|)    
## (Intercept)                               4e-08 ***
## day                                       0.004 ** 
## patch_typeLarge connected to large        0.385    
## day:patch_typeLarge connected to large    0.089 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lctl
## day         -0.903              
## ptch_tyLctl -0.816  0.737       
## dy:ptc_Lctl  0.737 -0.816 -0.903
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## day            34.918  34.918     1    15 55.0124 2.159e-06 ***
## patch_type      0.508   0.508     1    15  0.8008   0.38499    
## day:patch_type  2.100   2.100     1    15  3.3084   0.08894 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.3   0.071   * weak

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    248.1    265.6   -117.1    234.1       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.02  -0.58  -0.02   0.48   2.41 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 0.535    0.73          
##             day         0.002    0.04     -0.90
##  Residual               0.635    0.80          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            5.59       0.35 20.62      16    4e-13
## day                                   -0.13       0.02 15.00      -8    1e-06
## patch_typeLarge connected to large    -0.44       0.25 15.00      -2      0.1
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeLarge connected to large    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.801       
## ptch_tyLctl -0.488  0.000
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.3    0.13     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Shannon

response_variable = "shannon"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##      5.5     25.5      5.3    -10.5       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.76  -0.60  -0.06   0.78   1.59 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e-02    0.148         
##             day         8e-05    0.009    -1.00
##  Residual               5e-02    0.221         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               1.831      0.132 16.742      14
## day                                      -0.017      0.007 15.185      -2
## patch_typeLarge connected to large       -0.332      0.161 16.742      -2
## day:patch_typeLarge connected to large    0.013      0.009 15.185       2
##                                        Pr(>|t|)    
## (Intercept)                               1e-10 ***
## day                                        0.03 *  
## patch_typeLarge connected to large         0.06 .  
## day:patch_typeLarge connected to large     0.15    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lctl
## day         -0.951              
## ptch_tyLctl -0.816  0.777       
## dy:ptc_Lctl  0.777 -0.816 -0.951
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            0.27607 0.27607     1 15.184  5.6629 0.03085 *
## patch_type     0.20643 0.20643     1 16.742  4.2345 0.05553 .
## day:patch_type 0.11339 0.11339     1 15.184  2.3259 0.14780  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.5   0.064   * weak

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##      5.6     23.1      4.2     -8.4       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.73  -0.67  -0.01   0.77   1.68 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 3e-02    0.18          
##             day         1e-04    0.01     -1.00
##  Residual               5e-02    0.22          
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           1.675      0.087 21.683      19    5e-15
## day                                  -0.008      0.004 15.074      -2     0.08
## patch_typeLarge connected to large   -0.098      0.050 66.017      -2     0.05
##                                       
## (Intercept)                        ***
## day                                .  
## patch_typeLarge connected to large .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.886       
## ptch_tyLctl -0.379  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -1.3   0.068   * weak
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Richness

response_variable = "species_richness"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+00
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
## Warning: Model failed to converge with 1 negative eigenvalue: -7.2e-01
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    302.6    322.6   -143.3    286.6       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.6    0.1    0.5    2.3 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00    0.00         
##             day         5e-04    0.02      NaN
##  Residual               1e+00    1.13         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error    df t value
## (Intercept)                                7.55       0.58 72.97    13.0
## day                                       -0.02       0.03 34.65    -0.7
## patch_typeLarge connected to large        -0.34       0.71 72.97    -0.5
## day:patch_typeLarge connected to large    -0.03       0.04 34.65    -0.7
##                                        Pr(>|t|)    
## (Intercept)                              <2e-16 ***
## day                                         0.5    
## patch_typeLarge connected to large          0.6    
## day:patch_typeLarge connected to large      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lctl
## day         -0.888              
## ptch_tyLctl -0.816  0.725       
## dy:ptc_Lctl  0.725 -0.816 -0.888
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            3.9602  3.9602     1 34.648  3.1048 0.08688 .
## patch_type     0.2862  0.2862     1 72.965  0.2244 0.63713  
## day:patch_type 0.5569  0.5569     1 34.648  0.4366 0.51313  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1     -0.9   0.085   * weak

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##    301.0    318.5   -143.5    287.0       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.1   -0.6    0.2    0.5    2.3 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr
##  culture_ID (Intercept) 0e+00    0.00         
##             day         5e-04    0.02      NaN
##  Residual               1e+00    1.13         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error    df t value Pr(>|t|)
## (Intercept)                            7.83       0.40 65.64      20   <2e-16
## day                                   -0.04       0.02 31.73      -2     0.04
## patch_typeLarge connected to large    -0.75       0.33 15.15      -2     0.04
##                                       
## (Intercept)                        ***
## day                                *  
## patch_typeLarge connected to large *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.744       
## ptch_tyLctl -0.545  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1     -2.5   0.034 ** moderate
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Evenness

response_variable = "evenness_pielou"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -155.5   -135.5     85.8   -171.5       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.4   -0.5    0.1    0.6    1.6 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 9e-03    0.095         
##             day         3e-05    0.006    -1.00
##  Residual               8e-03    0.088         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error     df t value
## (Intercept)                               0.911      0.062 16.136      15
## day                                      -0.007      0.003 15.077      -2
## patch_typeLarge connected to large       -0.155      0.076 16.136      -2
## day:patch_typeLarge connected to large    0.009      0.004 15.077       2
##                                        Pr(>|t|)    
## (Intercept)                               9e-11 ***
## day                                        0.05 .  
## patch_typeLarge connected to large         0.06 .  
## day:patch_typeLarge connected to large     0.05 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lctl
## day         -0.966              
## ptch_tyLctl -0.816  0.789       
## dy:ptc_Lctl  0.789 -0.816 -0.966
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## day            0.013969 0.013969     1 15.077  1.8244 0.19672  
## patch_type     0.032213 0.032213     1 16.136  4.2069 0.05686 .
## day:patch_type 0.033595 0.033595     1 15.077  4.3875 0.05350 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      0.1   0.145     none

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   -153.7   -136.2     83.8   -167.7       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -3.3   -0.5    0.1    0.7    1.6 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e-02    0.119         
##             day         5e-05    0.007    -1.00
##  Residual               8e-03    0.088         
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)                           0.809      0.042 18.815    19.1    9e-14
## day                                  -0.001      0.002 15.031    -0.6      0.6
## patch_typeLarge connected to large   -0.002      0.020 72.123    -0.1      0.9
##                                       
## (Intercept)                        ***
## day                                   
## patch_typeLarge connected to large    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.925       
## ptch_tyLctl -0.310  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1        2   0.914     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Median Size

response_variable = "median_body_area_µm2"
filtered_data = ds_patches %>%
  filter(time_point >= first_time_point_model,
         time_point <= last_time_point_model,
         patch_type %in% patch_type_i,
         !is.na(!!sym(response_variable)))
plot.patches.points(filtered_data,
                    patch_type_i,
                    response_variable) 

Null model
null_model = lmer(
  get(response_variable) ~
    day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Full model (with patch_type and patch_type : day)
full_model = lmer(
  get(response_variable) ~
    day + 
    patch_type +
    patch_type : day + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(full_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +  
##     (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1383.4   1403.4   -683.7   1367.4       82 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##  -2.02  -0.62  -0.07   0.49   3.03 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 1e+05    315           
##             day         5e+02     22      -1.00
##  Residual               2e+05    457           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                        Estimate Std. Error   df t value
## (Intercept)                                3236        274   24      12
## day                                          17         16   17       1
## patch_typeLarge connected to large         -848        336   24      -2
## day:patch_typeLarge connected to large       54         19   17       3
##                                        Pr(>|t|)    
## (Intercept)                               2e-11 ***
## day                                        0.29    
## patch_typeLarge connected to large         0.02 *  
## day:patch_typeLarge connected to large     0.01 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day    p_Lctl
## day         -0.946              
## ptch_tyLctl -0.816  0.773       
## dy:ptc_Lctl  0.773 -0.816 -0.946
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
Click for model ANOVA
anova(full_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## day            4437335 4437335     1 17.105 21.2356 0.0002469 ***
## patch_type     1330309 1330309     1 23.488  6.3664 0.0188303 *  
## day:patch_type 1646085 1646085     1 17.105  7.8776 0.0120813 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(full_model),
           residuals = resid(full_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
                                       null_model,
                                       "mixed_model")

model_stats_full %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value    evidence
## 1     -2.5   0.039 ** moderate

 
 
 

Reduced model (with patch_type)
reduced_model = lmer(
  get(response_variable) ~
    day + 
    patch_type + 
    (day | culture_ID), 
  data = filtered_data,
  REML = FALSE,
  control = lmerControl(optimizer = "Nelder_Mead")
)
Click for model output
print(summary(reduced_model), digits = 1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
##    Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
## 
##      AIC      BIC   logLik deviance df.resid 
##   1387.8   1405.3   -686.9   1373.8       83 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
##   -1.9   -0.6   -0.2    0.5    3.0 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev. Corr 
##  culture_ID (Intercept) 2e+05    497           
##             day         1e+03     33      -1.00
##  Residual               2e+05    459           
## Number of obs: 90, groups:  culture_ID, 15
## 
## Fixed effects:
##                                    Estimate Std. Error   df t value Pr(>|t|)
## (Intercept)                            2651        201   24    13.2    2e-12
## day                                      54         11   16     4.8    2e-04
## patch_typeLarge connected to large       30        108   52     0.3      0.8
##                                       
## (Intercept)                        ***
## day                                ***
## patch_typeLarge connected to large    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) day   
## day         -0.897       
## ptch_tyLctl -0.360  0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#Create resid vs fitted
res_vs_fit = filtered_data %>%
    mutate(predicted = fitted(reduced_model),
           residuals = resid(reduced_model)) %>%
    plot_ly(x = ~predicted,
            y = ~residuals,
            type = "scatter",
            mode = "markers",
            marker = list(size = 5, color = "#4C78A8"),
            text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
                         "Day: ", filtered_data$day, "<br>",
                         "Meta-ecosystem: ", filtered_data$patch_type, "<br>",
                         "Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
            hoverinfo = "text") %>%
    plotly::layout(
        xaxis = list(title = "Fitted Values"),
        yaxis = list(title = "Residuals")
    )
Click for residual plots
res_vs_fit
qqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
                                       null_model,
                                       "mixed_model")

model_stats_fixed %>%
  mutate(deltaAIC = round(deltaAIC, digits = 1),
         p_value = round(p_value, digits = 3),
         R2 = NULL,
         evidence = "",
         evidence = ifelse(p_value > 0.1, 
                           "none",
                           evidence),
         evidence = ifelse(p_value < 0.1, 
                           "* weak",
                           evidence),
         evidence = ifelse(p_value < 0.05, 
                           "** moderate",
                           evidence),
         evidence = ifelse(p_value < 0.01, 
                           "*** strong",
                           evidence),
         evidence = ifelse(p_value < 0.001, 
                           "**** very strong",
                           evidence),
         p_value = ifelse(p_value < 0.001,
                           "< 0.001",
                           p_value)) %>%
  print()
##   deltaAIC p_value evidence
## 1      1.9   0.808     none
results_table = fill.results.table(
  results_table,
  response_variable,
  patch_type_i,
  model_stats_full,
  model_stats_fixed
)

Populations

for(patch_type_i in patch_type_i){
  
  print(patch_type_i)

  p = ds_patches %>%
  filter(patch_type == patch_type_i) %>%
  group_by(day) %>%
  summarise(
    Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
    # Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
    # Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
    Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
    Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
    Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
    Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
    Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
    Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
    Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
    Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
    Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
    Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
  ) %>%
  pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
  ggplot(aes(
    x = day,
    y = species_indiv_per_ml,
    group = interaction(day, species),
    color = species
  )) +
  geom_point(position = position_dodge(dodging),
             size = treatment_points_size) +
  geom_line(
    aes(group = species),
    position = position_dodge(dodging),
    linewidth = treatment_lines_linewidth) + 
  labs(x = axis_names %>% 
         filter(variable == "day") %>% 
         pull(axis_name),
       y = axis_names %>% 
         filter(variable == "dominance") %>% 
         pull(axis_name)) +
  coord_cartesian(ylim = c(0, 100))
  
  print(p)

}
## [1] "Large connected to large"

## [1] "Large isolated"

Results

Meta-ecosystems

# Construct plots

response_variable_list = NULL
n_of_plots = 3
response_variable_list[[1]] = "mean_shannon"
response_variable_list[[2]] = "bray_curtis"
response_variable_list[[3]] = "metaecosystem_richness"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
                         "Medium-Medium isolated",
                         "Small-Large meta-ecosystem",
                         "Small-Large isolated")
legend_row_n_input = 2
p = NULL
  
for (i in 1: n_of_plots) {
  
  p[[i]] = plot.metaecos.points(ds_metaecosystems,
                               metaecosystem_type_i,
                               response_variable_list[[i]],
                               legend_row_n_input) +
    theme(plot.margin = unit(c(ggarrange_margin_left,
                               ggarrange_margin_right,
                               ggarrange_margin_bottom,
                               ggarrange_margin_left),
                             "cm"))
}
# Combine plots

p_combined = ggarrange(
  p[[1]] +
    rremove("xlab") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank()) +
    font("legend.text", size = size_legend) +
    font("ylab", size = size_y_axis),
  p[[2]] +
    rremove("xlab") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank()) +
    font("legend.text", size = size_legend) +
    font("ylab", size = size_y_axis),
  p[[3]] +
    font("legend.text", size = size_legend) +
    font("xlab", size = size_y_axis) +
    font("ylab", size = size_y_axis) +
    scale_x_continuous(breaks = unique(ds_metaecosystems$day)),
  heights = c(0.8,0.8,1),
  nrow = 3,
  common.legend = TRUE,
  align = "v",
  labels = c("(a)", "(b)", "(c)"),
  label.x = 0.1,
  label.y = 0.8
)
# Show combined plots

p_combined

# Save combined plots

png(
  file = here("results", "figures", disturbance_global_input, "paper", "metaecosystems_diversity.png"),
  width = paper_width,
  height = paper_height,
  units = paper_units,
  res = paper_res
)

p_combined

dev.off()
# Plots parameters
legend_row_n_input = 2

x_min = -0.2
x_max = 30

y_min_alpha = 0.3
y_max_alpha = 2.1

y_min_beta = 0
y_max_beta = 1

size_y_axis = 22

metaecosystem_type_all = c("Medium-Medium isolated",
                           "Medium-Medium meta-ecosystem",
                           "Small-Large isolated",
                           "Small-Large meta-ecosystem")
# Construct alpha plots

response_variable = "mean_shannon"
alpha_plots = NULL

alpha_plots[[1]] = plot.empty.plot.presentations(y_min_alpha,
                                                 y_max_alpha,
                                                 "mean_shannon")

for (i in 1:length(metaecosystem_type_all)) {
  
  alpha_plots[[i+1]] = plot.metaecos.points.presentations(ds_metaecosystems,
                                                          metaecosystem_type_all[1:i],
                                                          response_variable,
                                                          legend_row_n_input) +
    theme(plot.margin = unit(c(ggarrange_margin_left,
                               ggarrange_margin_right,
                               ggarrange_margin_bottom,
                               ggarrange_margin_left),
                             "cm"),
          legend.position = "none") +
    scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) + 
    xlim(x_min, x_max) +
    ylim(y_min_alpha, y_max_alpha)
  
  }
# Construct beta plots

response_variable = "bray_curtis"
beta_plots = NULL

beta_plots[[1]] = plot.empty.plot.presentations(y_min_beta,
                                                y_max_beta,
                                                "bray_curtis")

for (i in 1:length(metaecosystem_type_all)) {
  
  beta_plots[[i+1]] = plot.metaecos.points.presentations(ds_metaecosystems,
                                                         metaecosystem_type_all[1:i],
                                                         response_variable,
                                                         legend_row_n_input) +
    theme(plot.margin = unit(c(ggarrange_margin_left,
                               ggarrange_margin_right,
                               ggarrange_margin_bottom,
                               ggarrange_margin_left),
                             "cm"),
          legend.position = "none") +
    scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
    xlim(x_min, x_max) +
    ylim(y_min_beta, y_max_beta)
  
  }
# Combine plots

p_combined = NULL
alpha_beta_plot_number = data.frame(alpha = as.double(c("1", "2", "3", "4", "5", "5", "5", "5", "5")),
                                    beta = as.double(c("1", "1", "1", "1", "1", "2", "3", "4", "5")))

for(i in 1:length(alpha_beta_plot_number$alpha)){
  
  p_combined[[i]] = ggarrange(
  alpha_plots[[alpha_beta_plot_number$alpha[i]]] +
    rremove("xlab") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.text = element_text(size = presentation_axes_size)) +
    font("legend.text", size = size_legend) +
    font("ylab", size = size_y_axis),
  NULL,
  beta_plots[[alpha_beta_plot_number$beta[i]]] +
    theme(axis.text = element_text(size = presentation_axes_size)) + 
    font("legend.text", size = size_legend) +
    font("xlab", size = size_y_axis) +
    font("ylab", size = size_y_axis),
  heights = c(0.999, 0.001, 0.999),
  nrow = 3,
  align = "v",
  label.x = 0.1,
  label.y = 0.8)

  }
# Save combined plots

for(i in 1:length(alpha_beta_plot_number$alpha)){
  
  png(file = here("results", "figures", disturbance_global_input, "presentations", paste0(i, "_metaecos_diversity.png")),
      width = presentation_figure_width,
      height = presentation_figure_height,
      units = presentation_figure_units,
      res = presentation_figure_res)
  
  print(p_combined[[i]])
  
  dev.off()
  
  }

Isolated patches

# Construct plots

response_variable_list = NULL
response_variable_list[[1]] = "bioarea_mm2_per_ml"
response_variable_list[[2]] = "shannon"
patch_type_i = c("Small isolated",
                 "Medium isolated",
                 "Large isolated")
legend_row_n_input = 2
p = NULL
  
for (i in 1:2) {
  
  p[[i]] = plot.patches.points(ds_patches,
                               patch_type_i,
                               response_variable_list[[i]],
                               legend_row_n_input) +
    theme(plot.margin = unit(c(ggarrange_margin_left,
                               ggarrange_margin_right,
                               ggarrange_margin_bottom,
                               ggarrange_margin_left),
                             "cm"))
}
# Combine plots

p_combined = ggarrange(
  p[[1]] +
    rremove("xlab") +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank()) +
    font("legend.text", size = size_legend) +
    font("ylab", size = size_y_axis),
  p[[2]] +
    font("legend.text", size = size_legend) +
    font("xlab", size = size_y_axis) +
    font("ylab", size = size_y_axis) +
    scale_x_continuous(breaks = unique(ds_patches$day)),
  heights = c(0.8, 0.8, 1),
  nrow = 2,
  align = "v",
  labels = c("(a)", "(b)"),
  label.x = 0.1,
  label.y = 0.8,
  common.legend = TRUE
)
# Show combined plots

p_combined

# Save combined plots

png(
  file = here("results", "figures", disturbance_global_input, "paper", "patches_biomass.png"),
  width = paper_width,
  height = paper_height,
  units = paper_units,
  res = paper_res
)

p_combined

dev.off()

Evaporation

Evaporation when microwaving 15 falcon tubes at the time

evaporation.test = read.csv(here("data", "evaporation_test","evaporation_test_right.csv"), header = TRUE)

evaporation.test %>%
  ggplot(aes (x = as.character(water_pipetted),
                y = weight_water_evaporated,
                group = interaction(water_pipetted, as.character(rack)),
                fill = as.character(rack))) +
  geom_boxplot(width = boxplot_width) +
  labs(x = "Water volume (ml)" , 
       y = "Evaporation (g)", 
       fill = "Rack replicate")

Evaporation when microwaving 5 tubes with 10 filled or empty tubes

evaporation.test = read.csv(here("data", "evaporation_test", "evaporation_test_fill_nofill.csv"), header = TRUE)

evaporation.test %>%
  ggplot(aes (x = all_tubes_water,
              y = weight_water_evaporated)) +
  geom_boxplot(width = boxplot_width) +
  labs(x = "Water in the other 10 tubes" , 
  y = "Evaporation (g)", 
  caption = "When all tubes were filled, they were filled with 6.75 ml of deionised water.")

Analysis scripts

Video analysis

rm(list = ls())
setwd("/media/mendel-himself/ID_061_Ema2/PatchSizePilot/training")

# load package
# library(devtools)
# install_github("femoerman/bemovi", ref="master")
library(bemovi)
library(parallel)
library(doParallel)
library(foreach)

#Define memory to be allocated
memory.alloc <- 240000 #-needs_to_be_specified
memory.per.identifier <- 40000 #-needs_to_be_specified
memory.per.linker <- 5000 #-needs_to_be_specified
memory.per.overlay <- 60000 #-needs_to_be_specified

# UNIX
# set paths to tools folder and particle linker
tools.path <-
  "/home/mendel-himself/bemovi_tools/" #-needs_to_be_specified
to.particlelinker <- tools.path

# directories and file names
to.data <- paste(getwd(), "/", sep = "")
video.description.folder <- "0_video_description/"
video.description.file <- "video_description.txt"
raw.video.folder <- "1_raw/"
raw.avi.folder <- "1a_raw_avi/"
metadata.folder <- "1b_raw_meta/"
particle.data.folder <- "2_particle_data/"
trajectory.data.folder <- "3_trajectory_data/"
temp.overlay.folder <- "4a_temp_overlays/"
overlay.folder <- "4_overlays/"
merged.data.folder <- "5_merged_data/"
ijmacs.folder <- "ijmacs/"


######################################################################
# VIDEO PARAMETERS

# video frame rate (in frames per second)
fps <- 25 #-needs_to_be_specified

# length of video (in frames)
total_frames <- 125 #-needs_to_be_specified

#Dimensions of the videos in pixels
width = 2048 #-needs_to_be_specified
height = 2048 #-needs_to_be_specified

# measured volume (in microliter) #-needs_to_be_specified
measured_volume <-
  34.4 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#measured_volume <- 14.9 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III

# size of a pixel (in micrometer) #-needs_to_be_specified
pixel_to_scale <-
  4.05 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#pixel_to_scale <- 3.79 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III

# specify video file format (one of "avi","cxd","mov","tiff")
# bemovi only works with avi and cxd. other formats are reformated to avi below
video.format <- "cxd" #-needs_to_be_specified

# setup
difference.lag <- 10
thresholds <- c(13, 255) # don't change the second value
# thresholds <- c(50,255)

# MORE PARAMETERS (USUALLY NOT CHANGED)
######################################################################
# FILTERING PARAMETERS
# optimized for Perfex Pro 10 stereomicrocope with Perfex SC38800 (IDS UI-3880LE-M-GL) camera
# tested stereomicroscopes: Perfex Pro 10, Nikon SMZ1500, Leica M205 C
# tested cameras: Perfex SC38800, Canon 5D Mark III, Hamamatsu Orca Flash 4
# tested species: Tet, Col, Pau, Pca, Eug, Chi, Ble, Ceph, Lox, Spi

# min and max size: area in pixels
particle_min_size <- 10
particle_max_size <- 1000

# number of adjacent frames to be considered for linking particles
trajectory_link_range <- 3
# maximum distance a particle can move between two frames
trajectory_displacement <- 16

# these values are in the units defined by the parameters above: fps (seconds), measured_volume (microliters) and pixel_to_scale (micometers)
filter_min_net_disp <- 25
filter_min_duration <- 1
filter_detection_freq <- 0.1
filter_median_step_length <- 3

######################################################################
# VIDEO ANALYSIS

#Check if all tools are installed, and if not install them
check_tools_folder(tools.path)

#Ensure computer has permission to run bftools
system(paste0("chmod a+x ", tools.path, "bftools/bf.sh"))
system(paste0("chmod a+x ", tools.path, "bftools/bfconvert"))
system(paste0("chmod a+x ", tools.path, "bftools/showinf"))

# Convert files to compressed avi (takes approx. 2.25 minutes per video)
convert_to_avi(
  to.data,
  raw.video.folder,
  raw.avi.folder,
  metadata.folder,
  tools.path,
  fps,
  video.format
)


# TESTING

# check file format and naming
# check_video_file_names(to.data,raw.avi.folder,video.description.folder,video.description.file)

# check whether the thresholds make sense (set "dark backgroud" and "red")
#check_threshold_values(to.data, raw.avi.folder, ijmacs.folder, 2, difference.lag, thresholds, tools.path,  memory.alloc)

# identify particles
locate_and_measure_particles(
  to.data,
  raw.avi.folder,
  particle.data.folder,
  difference.lag,
  min_size = particle_min_size,
  max_size = particle_max_size,
  thresholds = thresholds,
  tools.path,
  memory = memory.alloc,
  memory.per.identifier = memory.per.identifier,
  max.cores = detectCores() - 1
)

# link the particles
link_particles(
  to.data,
  particle.data.folder,
  trajectory.data.folder,
  linkrange = trajectory_link_range,
  disp = trajectory_displacement,
  start_vid = 1,
  memory = memory.alloc,
  memory_per_linkerProcess = memory.per.linker,
  raw.avi.folder,
  max.cores = detectCores() - 1,
  max_time = 1
)

# merge info from description file and data
merge_data(
  to.data,
  particle.data.folder,
  trajectory.data.folder,
  video.description.folder,
  video.description.file,
  merged.data.folder
)

# load the merged data
load(paste0(to.data, merged.data.folder, "Master.RData"))

# filter data: minimum net displacement, their duration, the detection frequency and the median step length
trajectory.data.filtered <-
  filter_data(
    trajectory.data,
    filter_min_net_disp,
    filter_min_duration,
    filter_detection_freq,
    filter_median_step_length
  )

# summarize trajectory data to individual-based data
morph_mvt <-
  summarize_trajectories(
    trajectory.data.filtered,
    calculate.median = F,
    write = T,
    to.data,
    merged.data.folder
  )

# get sample level info
summarize_populations(
  trajectory.data.filtered,
  morph_mvt,
  write = T,
  to.data,
  merged.data.folder,
  video.description.folder,
  video.description.file,
  total_frames
)

# create overlays for validation
create.subtitle.overlays(
  to.data,
  traj.data = trajectory.data.filtered,
  raw.video.folder,
  raw.avi.folder,
  temp.overlay.folder,
  overlay.folder,
  fps,
  vid.length = total_frames / fps,
  width,
  height,
  tools.path = tools.path,
  overlay.type = "number",
  video.format
)

# Create overlays (old method)
create_overlays(
  traj.data = trajectory.data.filtered,
  to.data = to.data,
  merged.data.folder = merged.data.folder,
  raw.video.folder = raw.avi.folder,
  temp.overlay.folder = "4a_temp_overlays_old/",
  overlay.folder = "4_overlays_old/",
  width = width,
  height = height,
  difference.lag = difference.lag,
  type = "traj",
  predict_spec = F,
  contrast.enhancement = 1,
  IJ.path = "/home/mendel-himself/bemovi_tools",
  memory = memory.alloc,
  max.cores = detectCores() - 1,
  memory.per.overlay = memory.per.overlay
)

########################################################################
# some cleaning up
#system("rm -r 2_particle_data")
#system("rm -r 3_trajectory_data")
#system("rm -r 4a_temp_overlays")
system("rm -r ijmacs")
########################################################################

Species identification

rm(list = ls())

#install.packages("e1071",dependencies = T)
#install.packages("devtools",dependencies = T)
#install_github("pennekampster/bemovi", ref="master")
#library(devtools)

library(bemovi)
library(e1071)
library("here")
library("tidyverse")

time_points_in_experiment = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")

for (time_point in time_points_in_experiment) {
  
  #Folder names and paths
  video.description.folder = "0_video_description/"
  video.description.file = "video_description.txt"
  merged.data.folder = "5_merged_data/"
  monocultures_folder_path = here("biomass_analysis", "training", "")
  mixed_cultures_folder_path = here("biomass_analysis", time_point, "")
  
  #Parameters used in the video analysis script
  fps = 25
  nsv = 5
  measured_volume = 34.4
  pixel_to_scale = 4.05
  filter_min_net_disp = 25
  filter_min_duration = 1
  filter_detection_freq = 0.1
  filter_median_step_length = 3
  
  #Load master dataset of mono-cultures
  load(paste0(
    monocultures_folder_path,
    merged.data.folder,
    "Master.RData"
  ))
  trajectory.data_monocultures = trajectory.data
  rm(trajectory.data)
  
  #Filter the master data of mono-cultures using the same parameters as in the videos analysis script
  trajectory.data_monocultures.filtered = filter_data(
    trajectory.data_monocultures,
    filter_min_net_disp,
    filter_min_duration,
    filter_detection_freq,
    filter_median_step_length
  )
  
  #Go from trajectory data to individuals data
  morph_mvt = summarize_trajectories(
    data = trajectory.data_monocultures.filtered,
    calculate.median = FALSE,
    write = TRUE,
    to.data = monocultures_folder_path,
    merged.data.folder = merged.data.folder
  ) %>%
    mutate(comment = NULL)
  
  training_data = morph_mvt[complete.cases(morph_mvt), ]
  
  svm1 = svm(
    factor(species) ~
      mean_grey +
      sd_grey +
      mean_area +
      sd_area +
      mean_perimeter +
      mean_turning +
      sd_turning +
      sd_perimeter +
      mean_major +
      sd_major +
      mean_minor +
      sd_minor +
      mean_ar +
      sd_ar +
      duration +
      max_net  +
      net_disp +
      net_speed +
      gross_disp +
      max_step +
      min_step +
      sd_step +
      sd_gross_speed +
      max_gross_speed +
      min_gross_speed ,
    data = training_data,
    probability = T,
    na.action = na.pass
  )
  
  confusion.matrix = table(svm1$fitted, training_data$species)
  confusion.matrix.nd = confusion.matrix
  diag(confusion.matrix.nd) = 0
  svm1$confusion = cbind(confusion.matrix,
                         class.error = rowSums(confusion.matrix.nd) / rowSums(confusion.matrix))
  
  print(paste("Confusion matrix of time point", time_point))
  print(svm1$confusion)
  
  species.names = unique(trajectory.data_monocultures$species)
  
  load(paste0(
    mixed_cultures_folder_path,
    merged.data.folder,
    "Master.RData"
  ))
  
  trajectory.data_mixed = trajectory.data
  rm(trajectory.data)
  
  trajectory.data_mixed.filtered = filter_data(
    trajectory.data_mixed,
    filter_min_net_disp,
    filter_min_duration,
    filter_detection_freq,
    filter_median_step_length
  )
  
  morph_mvt = summarize_trajectories(
    data = trajectory.data_mixed.filtered,
    calculate.median = FALSE,
    write = TRUE,
    to.data = mixed_cultures_folder_path,
    merged.data.folder = merged.data.folder
  )[, which(colnames(morph_mvt) != "Col_manual")] %>%
    mutate(comment = NULL)
  
  data.to.predict = morph_mvt[complete.cases(morph_mvt),]
  
  p.id = predict(object = svm1,
                 data.to.predict,
                 type = "response")
  
  data.to.predict$predicted_species = as.character(p.id)
  
  pop.data = summarize_populations(
    traj.data = trajectory.data_monocultures.filtered,
    sum.data = morph_mvt,
    write = TRUE,
    to.data = mixed_cultures_folder_path,
    merged.data.folder = merged.data.folder,
    video.description.folder = video.description.folder,
    video.description.file = video.description.file,
    total_frame = fps * nsv
  )
  
  species.density = function(sample_output,
                             indiv_predicted,
                             species_names,
                             total_frames,
                             mv = measured_volume) {
    samples = unique(indiv_predicted$file)
    
    sp.dens = matrix(0,
                     nrow(sample_output),
                     length(species_names))
    
    colnames(sp.dens) = species_names
    
    for (i in 1:length(samples)) {
      indiv = subset(indiv_predicted, file == samples[i])
      
      spec = unique(indiv$predicted_species)
      
      for (j in 1:length(spec)) {
        all.indiv.sp = subset(indiv,
                              predicted_species == spec[j])
        
        dens = sum(all.indiv.sp$N_frames) / total_frames / mv
        sp.dens[which(sample_output$file == as.character(samples[i])), which(species_names == spec[j])] = dens
      }
    }
    
    return(cbind(sample_output, sp.dens))
    
  }
  
  output = species.density(
    pop.data,
    data.to.predict,
    species.names,
    total_frames = fps * nsv,
    mv = measured_volume
  )
  
  file_name = paste0("species_ID_", time_point, ".csv")
  
  write.csv(output,
            here("biomass_analysis", "species_ID_results", file_name))
  rm(output)
  
}

Other

Running time

## Time difference of 1.8 mins

Disturbance input

Check that disturbance_global_input is what you set:

print(paste0("Disturbance = ", disturbance_global_input))
## [1] "Disturbance = low"

Change code using terminal

If you want to change a certain part of the code:

#Rmd script
cd /Users/ema/Documents/GitHub/PatchSize/r_files
sed -i '' 's/old_string/new_string/g' *.Rmd

#R script
cd /Users/ema/Documents/GitHub/PatchSize/r_files/functions
sed -i '' 's/old_string/new_string/g' *.R

Code to give you back an object

dput()

Crashed cultures

The only type of patch where all cultures crashed was small connected to small at high disturbance.

Residuals

References